miχpods: MOM6

• bootstrap error on mean, median?

• Try daily vs hourly

• add TAO χpods

• fix EUC max at 110

Questions:

• N2 vs N2T

• match time intervals

• match vertical resolution and extent

• restrict TAO S2 to ADCP depth range

• restrict to top 200m.

Notes:

• Filtering out MLD makes a big difference!

• SInce we’re working with derivatives does the vertical grid matter (as long as it is coarser than observations)?

1 difvho ocean_vertical_heat_diffusivity 
2 difvso ocean_vertical_salt_diffusivity

• Need lateral / neutral terms for total χ, ε

• Why can’t I reproduce the ε figure?

• Why can’t I save the new station data

References

Warner & Moum (2019)

Setup

%load_ext watermark

import datetime
import glob
import os
import warnings
import pandas as pd

import cf_xarray
import dask
import dcpy
import distributed
import flox.xarray
import holoviews as hv
import hvplot.xarray
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
import xgcm

import pump
from pump import mixpods

hv.notebook_extension('bokeh')

dask.config.set({"array.slicing.split_large_chunks": False})
plt.style.use("bmh")
plt.rcParams["figure.dpi"] = 140 
xr.set_options(keep_attrs=True, display_expand_data=False)

gcmdir = "/glade/campaign/cgd/oce/people/bachman/TPOS_1_20_20_year/OUTPUT/"  # MITgcm output directory
stationdirname = gcmdir

%watermark -iv

The watermark extension is already loaded. To reload it, use:
  %reload_ext watermark

pump         : 0.1
distributed  : 2022.7.0
numpy        : 1.22.4
ncar_jobqueue: 2021.4.14
cf_xarray    : 0.7.4
dask_jobqueue: 0.7.3
dask         : 2022.7.0
pandas       : 1.4.3
ipywidgets   : 7.7.1
flox         : 0.5.9
json         : 2.0.9
holoviews    : 1.14.9
xgcm         : 0.6.0
hvplot       : 0.8.0
xarray       : 2022.6.0rc0
sys          : 3.10.5 | packaged by conda-forge | (main, Jun 14 2022, 07:06:46) [GCC 10.3.0]
dcpy         : 0.1.dev385+g121534c
matplotlib   : 3.5.2

import ncar_jobqueue

if "client" in locals():
    client.close()
    del client
if "cluster" in locals():
    cluster.close()

#env = {"OMP_NUM_THREADS": "3", "NUMBA_NUM_THREADS": "3"}

# cluster = distributed.LocalCluster(
#    n_workers=8,
#    threads_per_worker=1,
#    env=env
# )

if "cluster" in locals():
    del cluster

#cluster = ncar_jobqueue.NCARCluster(
#    project="NCGD0011",
#    scheduler_options=dict(dashboard_address=":9797"),
#)
# cluster = dask_jobqueue.PBSCluster(
#    cores=9, processes=9, memory="108GB", walltime="02:00:00", project="NCGD0043",
#    env_extra=env,
# )

import dask_jobqueue

cluster = dask_jobqueue.PBSCluster(
    cores=1, # The number of cores you want
    memory='23GB', # Amount of memory
    processes=1, # How many processes
    queue='casper', # The type of queue to utilize (/glade/u/apps/dav/opt/usr/bin/execcasper)
    local_directory='$TMPDIR', # Use your local directory
    resource_spec='select=1:ncpus=1:mem=23GB', # Specify resources
    project='ncgd0011', # Input your project ID here
    walltime='02:00:00', # Amount of wall time
    interface='ib0', # Interface to use
)
cluster.adapt(minimum_jobs=2, maximum_jobs=12, wait_count=300)

<distributed.deploy.adaptive.Adaptive at 0x2b3c167bfa90>

Read data

TAO

tao_Ri = xr.load_dataarray("tao-hourly-Ri-seasonal-percentiles.nc").cf.guess_coord_axis()

chipod.dd

tao_gridded = (
    xr.open_dataset(
        os.path.expanduser("~/work/pump/zarrs/tao-gridded-ancillary.zarr"), chunks="auto", engine="zarr"
    )
    .sel(longitude=-140, time=slice("1996", None))
)
tao_gridded["depth"].attrs["axis"] = "Z"
tao_gridded["shallowest"].attrs.clear()

chipod = (
    dcpy.oceans.read_cchdo_chipod_file("~/datasets/microstructure/osu/chipods_0_140W.nc")
    .sel(time=slice("2015"))
    # move from time on the half hour to on the hour
    .coarsen(time=2, boundary="trim").mean()
    # "Only χpods between 29 and 69 m are used in this analysis as 
    # deeper χpods are more strongly influenced by the variability of zEUC than by surface forcing."
    # - Warner and Moum (2019)
    .sel(depth=slice(29, 69))
    .reindex(time=tao_gridded.time, method="nearest", tolerance="5min")
    .pipe(mixpods.normalize_z, sort=True)
)

tao_gridded = (
    tao_gridded
    .update(
        chipod[["chi", "KT", "eps", "Jq"]]
        .reset_coords(drop=True)
        .rename({"depth": "depthchi"})
    )
)

tao_gridded = mixpods.prepare(tao_gridded, oni=pump.obs.process_oni())

tao_gridded = tao_gridded.update(
    mixpods.pdf_N2S2(tao_gridded.drop_vars(["shallowest", "zeuc"]))
)
tao_gridded = mixpods.load(tao_gridded)

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:245: UserWarning: The specified Dask chunks separate the stored chunks along dimension "depth" starting at index 58. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:245: UserWarning: The specified Dask chunks separate the stored chunks along dimension "time" starting at index 139586. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:245: UserWarning: The specified Dask chunks separate the stored chunks along dimension "longitude" starting at index 2. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/core.py:119: RuntimeWarning: divide by zero encountered in true_divide
  return func(*(_execute_task(a, cache) for a in args))
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/core.py:119: RuntimeWarning: invalid value encountered in log10
  return func(*(_execute_task(a, cache) for a in args))
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/core.py:119: RuntimeWarning: divide by zero encountered in log10
  return func(*(_execute_task(a, cache) for a in args))
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/core.py:119: RuntimeWarning: divide by zero encountered in true_divide
  return func(*(_execute_task(a, cache) for a in args))
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/core.py:119: RuntimeWarning: invalid value encountered in log10
  return func(*(_execute_task(a, cache) for a in args))
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/core.py:119: RuntimeWarning: invalid value encountered in log10
  return func(*(_execute_task(a, cache) for a in args))
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/core.py:119: RuntimeWarning: invalid value encountered in log10
  return func(*(_execute_task(a, cache) for a in args))
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/core.py:119: RuntimeWarning: divide by zero encountered in true_divide
  return func(*(_execute_task(a, cache) for a in args))
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/flox/aggregations.py:258: RuntimeWarning: invalid value encountered in true_divide
  finalize=lambda sum_, count: sum_ / count,
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/core.py:119: RuntimeWarning: divide by zero encountered in log10
  return func(*(_execute_task(a, cache) for a in args))
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)

np.log10(tao_gridded.eps).hvplot.hist(bins=101)

np.log10(tao_gridded.eps).resample(time="M").mean().hvplot.quadmesh(clim=(-7.5, -6))

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/flox/aggregations.py:258: RuntimeWarning: invalid value encountered in true_divide
  finalize=lambda sum_, count: sum_ / count,

Possible reasons for difference

1. My MLD seems deeper even though I use the same ΔT=0.15 threshold. It could be that they’ve used Akima splines to interpolate in the vertical

2. I’ve filtered to DCL, so accounting for MLD and EUC movement. I’m not sure they did that.

Only 𝜒pods between 29 and 69 m are used in this analysis as deeper 𝜒pods are more strongly influenced by the variability of zEUC than by surface forcing.

da = np.log10(tao_gridded.eps_ri)
#da["Rig_T_bins"] = pd.Index(
#    [pd.Interval(np.log10(i.left), np.log10(i.right))
#     for i in tao_gridded.Rig_T_bins.data]
#)
(
    da
    .sel(stat="mean")
    .sel(enso_transition_phase=["La-Nina cool", "El-Nino warm"])
    .plot(hue="enso_transition_phase", ylim=(-7.6, -5.9), marker='.') 
)
ax = plt.gca()
ticks = [0.04, 0.1, 0.25, 0.63, 1.6]
ax.set_yticks([-7.25, -7, -6.5, -6])
ax.set_xticks(np.log10(ticks))
ax.set_xticklabels([f"{a:2f}" for a in ticks])
dcpy.plots.linex(np.log10(0.25))

(
    np.log10(tao_gridded.eps_ri)
    .sel(stat="mean")
    .sel(enso_transition_phase=["La-Nina cool", "El-Nino warm"])
    .plot(hue="enso_transition_phase", ylim=(-7.5, -6)) 
)

dcpy.plots.linex(0.25)

sub = tao_gridded.sel(time="2010-01")

t = sub.theta.hvplot.quadmesh(cmap="turbo_r")
dt = (sub.theta - sub.theta.reset_coords(drop=True).cf.sel(Z=[0, -5]).cf.max("Z")).hvplot.quadmesh(clim=(-0.15, 0.15), cmap="RdBu_r")
newmld = mixpods.get_mld_tao_theta(sub.theta.reset_coords(drop=True))

(dt
 * sub.reset_coords().mldT.hvplot.line(color='w', line_width=2)
 * newmld.reset_coords(drop=True).hvplot.line(color='orange', line_width=1)
).opts(frame_width=1200)

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)

cluster

(
    tao_gridded.reset_coords().mldT.resample(time="5D").mean().hvplot.line()
    * mixpods.get_mld_tao_theta((tao_gridded.reset_coords().theta)).resample(time="5D").mean().hvplot.line()
)

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/flox/aggregate_flox.py:105: RuntimeWarning: invalid value encountered in true_divide
  out /= nanlen(group_idx, array, size=size, axis=axis, fill_value=0)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/flox/aggregations.py:258: RuntimeWarning: invalid value encountered in true_divide
  finalize=lambda sum_, count: sum_ / count,

tao_gridded.u.cf.plot()
tao_gridded.eucmax.plot()

[<matplotlib.lines.Line2D at 0x2b38c6a4a8c0>]

MITgcm stations

stations = pump.model.read_stations_20(stationdirname)

gcmeq = stations.sel(longitude=[-155.025, -140.025, -125.025, -110.025], method="nearest")
#enso = pump.obs.make_enso_mask()
#mitgcm["enso"] = enso.reindex(time=mitgcm.time.data, method="nearest")

#gcmeq["eucmax"] = pump.calc.get_euc_max(gcmeq.u)
# pump.calc.calc_reduced_shear(gcmeq)

#oni = pump.obs.process_oni()
#gcmeq["enso_transition"] = mixpods.make_enso_transition_mask(oni).reindex(time=gcmeq.time.data, method="nearest")


mitgcm = gcmeq.sel(longitude=-140.025, method="nearest")
metrics = mixpods.normalize_z(pump.model.read_metrics(stationdirname), sort=True)
mitgcm = mixpods.normalize_z(mitgcm, sort=True)

mitgcm_grid = xgcm.Grid(
    metrics.sel(latitude=mitgcm.latitude, longitude=mitgcm.longitude, method="nearest"),
    coords=({"Z": {"center": "depth", "outer": "RF"}, "Y": {"center": "latitude"}}),
    metrics={"Z": ("drF", "drC")},
    periodic=False,
    boundary="fill",
    fill_value=np.nan,
)

mitgcm.theta.attrs["standard_name"]  = "sea_water_potential_temperature"
mitgcm["VISrI_Um"].attrs["standard_name"] = "ocean_vertical_x_viscosity"
mitgcm["VISrI_Vm"].attrs["standard_name"] = "ocean_vertical_y_viscosity"

mitgcm = mixpods.prepare(mitgcm, grid=mitgcm_grid, oni=pump.obs.process_oni()).sel(latitude=0, method="nearest").cf.sel(Z=slice(-250, 0))
mitgcm = mitgcm.update(mixpods.pdf_N2S2(mitgcm))
mitgcm = mixpods.load(mitgcm)
mitgcm

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask/array/core.py:4700: PerformanceWarning: Increasing number of chunks by factor of 37
  result = blockwise(

<xarray.Dataset>
Dimensions:                (depth: 100, time: 174000, RF: 101, N2T_bins: 29,
                            S2_bins: 29, enso_transition_phase: 7,
                            Rig_T_bins: 20, enso_2phase: 3, stat: 2)
Coordinates: (12/18)
  * depth                  (depth) float32 -248.8 -246.2 -243.8 ... -3.75 -1.25
    latitude               float64 0.025
    longitude              float64 -140.0
  * time                   (time) datetime64[ns] 1998-12-31T18:00:00 ... 2018...
  * RF                     (RF) float64 -250.0 -247.5 -245.0 ... -5.0 -2.5 0.0
    eucmax                 (time) float32 dask.array<chunksize=(162,), meta=np.ndarray>
    ...                     ...
  * S2_bins                (S2_bins) object (-5.0, -4.896551724137931] ... (-...
  * enso_transition_phase  (enso_transition_phase) object 'none' ... 'all'
    bin_areas              (N2T_bins, S2_bins) float64 0.0107 0.0107 ... 0.0107
  * Rig_T_bins             (Rig_T_bins) object (0.05, 0.1475] ... (1.9025, 2.0]
  * enso_2phase            (enso_2phase) object 'El-Nino' 'La-Nina' 'none'
  * stat                   (stat) <U4 'mean' 'std'
Data variables: (12/29)
    DFrI_TH                (depth, time) float32 dask.array<chunksize=(100, 6000), meta=np.ndarray>
    KPPdiffKzT             (depth, time) float32 dask.array<chunksize=(100, 6000), meta=np.ndarray>
    KPPg_TH                (depth, time) float32 dask.array<chunksize=(100, 6000), meta=np.ndarray>
    KPPhbl                 (time) float32 dask.array<chunksize=(6000,), meta=np.ndarray>
    KPPviscAz              (depth, time) float32 dask.array<chunksize=(100, 6000), meta=np.ndarray>
    SSH                    (time) float32 dask.array<chunksize=(6000,), meta=np.ndarray>
    ...                     ...
    shred2                 (RF, time) float32 dask.array<chunksize=(100, 6000), meta=np.ndarray>
    Rig_T                  (RF, time) float32 dask.array<chunksize=(100, 6000), meta=np.ndarray>
    eps                    (RF, time) float32 dask.array<chunksize=(100, 6000), meta=np.ndarray>
    n2s2pdf                (N2T_bins, S2_bins, enso_transition_phase) float64 ...
    eps_n2s2               (N2T_bins, S2_bins, enso_transition_phase) float64 ...
    eps_ri                 (stat, Rig_T_bins, enso_2phase) float64 0.3784 ......
Attributes:
    easting:   longitude
    northing:  latitude
    title:     Station profile, index (i,j)=(1201,240)

xarray.Dataset

• Dimensions:
  • depth: 100
  • time: 174000
  • RF: 101
  • N2T_bins: 29
  • S2_bins: 29
  • enso_transition_phase: 7
  • Rig_T_bins: 20
  • enso_2phase: 3
  • stat: 2
• Coordinates: (18)
  • depth
    (depth)
    float32
    -248.8 -246.2 ... -3.75 -1.25

    axis :

    Z

    unit :

    m

    positive :

    up

    array([-248.75, -246.25, -243.75, -241.25, -238.75, -236.25, -233.75, -231.25,
           -228.75, -226.25, -223.75, -221.25, -218.75, -216.25, -213.75, -211.25,
           -208.75, -206.25, -203.75, -201.25, -198.75, -196.25, -193.75, -191.25,
           -188.75, -186.25, -183.75, -181.25, -178.75, -176.25, -173.75, -171.25,
           -168.75, -166.25, -163.75, -161.25, -158.75, -156.25, -153.75, -151.25,
           -148.75, -146.25, -143.75, -141.25, -138.75, -136.25, -133.75, -131.25,
           -128.75, -126.25, -123.75, -121.25, -118.75, -116.25, -113.75, -111.25,
           -108.75, -106.25, -103.75, -101.25,  -98.75,  -96.25,  -93.75,  -91.25,
            -88.75,  -86.25,  -83.75,  -81.25,  -78.75,  -76.25,  -73.75,  -71.25,
            -68.75,  -66.25,  -63.75,  -61.25,  -58.75,  -56.25,  -53.75,  -51.25,
            -48.75,  -46.25,  -43.75,  -41.25,  -38.75,  -36.25,  -33.75,  -31.25,
            -28.75,  -26.25,  -23.75,  -21.25,  -18.75,  -16.25,  -13.75,  -11.25,
             -8.75,   -6.25,   -3.75,   -1.25], dtype=float32)

  • latitude
    ()
    float64
    0.025

    array(0.025)

  • longitude
    ()
    float64
    -140.0

    array(-140.0249939)

  • time
    (time)
    datetime64[ns]
    1998-12-31T18:00:00 ... 2018-11-...

    array(['1998-12-31T18:00:00.000000000', '1998-12-31T19:00:00.000000000',
           '1998-12-31T20:00:00.000000000', ..., '2018-11-06T15:00:32.000000000',
           '2018-11-06T16:00:16.000000000', '2018-11-06T17:00:00.000000000'],
          dtype='datetime64[ns]')

  • RF
    (RF)
    float64
    -250.0 -247.5 -245.0 ... -2.5 0.0

    long_name :

    depth

    units :

    m

    positive :

    up

    axis :

    Z

    array([-250. , -247.5, -245. , -242.5, -240. , -237.5, -235. , -232.5, -230. ,
           -227.5, -225. , -222.5, -220. , -217.5, -215. , -212.5, -210. , -207.5,
           -205. , -202.5, -200. , -197.5, -195. , -192.5, -190. , -187.5, -185. ,
           -182.5, -180. , -177.5, -175. , -172.5, -170. , -167.5, -165. , -162.5,
           -160. , -157.5, -155. , -152.5, -150. , -147.5, -145. , -142.5, -140. ,
           -137.5, -135. , -132.5, -130. , -127.5, -125. , -122.5, -120. , -117.5,
           -115. , -112.5, -110. , -107.5, -105. , -102.5, -100. ,  -97.5,  -95. ,
            -92.5,  -90. ,  -87.5,  -85. ,  -82.5,  -80. ,  -77.5,  -75. ,  -72.5,
            -70. ,  -67.5,  -65. ,  -62.5,  -60. ,  -57.5,  -55. ,  -52.5,  -50. ,
            -47.5,  -45. ,  -42.5,  -40. ,  -37.5,  -35. ,  -32.5,  -30. ,  -27.5,
            -25. ,  -22.5,  -20. ,  -17.5,  -15. ,  -12.5,  -10. ,   -7.5,   -5. ,
             -2.5,    0. ])

  • eucmax
    (time)
    float32
    dask.array<chunksize=(162,), meta=np.ndarray>

    units :

    m

    long_name :

    EUC maximum

    positive :

    up

    Array Chunk Bytes 679.69 kiB 672 B Shape (174000,) (168,) Count 229682 Tasks 1073 Chunks Type float32 numpy.ndarray

  • mldT
    (time)
    float64
    dask.array<chunksize=(6000,), meta=np.ndarray>

    long_name :

    MLD$_θ$

    units :

    m

    description :

    Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

    Array Chunk Bytes 1.33 MiB 46.88 kiB Shape (174000,) (6000,) Count 30107 Tasks 29 Chunks Type float64 numpy.ndarray

  • index
    (time)
    float32
    -1.57 -1.57 -1.57 ... 0.7 0.7 0.7

    array([-1.57, -1.57, -1.57, ...,  0.7 ,  0.7 ,  0.7 ], dtype=float32)

  • warm_mask
    (time)
    bool
    False False False ... True True

    array([False, False, False, ...,  True,  True,  True])

  • cool_mask
    (time)
    bool
    True True True ... False False

    array([ True,  True,  True, ..., False, False, False])

  • enso_transition
    (time)
    <U12
    'La-Nina cool' ... 'El-Nino warm'

    description :

    Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

    array(['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ...,
           'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12')

  • N2T_bins
    (N2T_bins)
    object
    (-5.0, -4.896551724137931] ... (...

    long_name :

    $N_T^2$

    units :

    s$^{-2}$

    array([Interval(-5.0, -4.896551724137931, closed='right'),
           Interval(-4.896551724137931, -4.793103448275862, closed='right'),
           Interval(-4.793103448275862, -4.689655172413793, closed='right'),
           Interval(-4.689655172413793, -4.586206896551724, closed='right'),
           Interval(-4.586206896551724, -4.482758620689655, closed='right'),
           Interval(-4.482758620689655, -4.379310344827586, closed='right'),
           Interval(-4.379310344827586, -4.275862068965517, closed='right'),
           Interval(-4.275862068965517, -4.172413793103448, closed='right'),
           Interval(-4.172413793103448, -4.068965517241379, closed='right'),
           Interval(-4.068965517241379, -3.9655172413793105, closed='right'),
           Interval(-3.9655172413793105, -3.862068965517241, closed='right'),
           Interval(-3.862068965517241, -3.7586206896551726, closed='right'),
           Interval(-3.7586206896551726, -3.655172413793103, closed='right'),
           Interval(-3.655172413793103, -3.5517241379310347, closed='right'),
           Interval(-3.5517241379310347, -3.4482758620689653, closed='right'),
           Interval(-3.4482758620689653, -3.344827586206897, closed='right'),
           Interval(-3.344827586206897, -3.2413793103448274, closed='right'),
           Interval(-3.2413793103448274, -3.137931034482759, closed='right'),
           Interval(-3.137931034482759, -3.0344827586206895, closed='right'),
           Interval(-3.0344827586206895, -2.9310344827586206, closed='right'),
           Interval(-2.9310344827586206, -2.8275862068965516, closed='right'),
           Interval(-2.8275862068965516, -2.7241379310344827, closed='right'),
           Interval(-2.7241379310344827, -2.6206896551724137, closed='right'),
           Interval(-2.6206896551724137, -2.5172413793103448, closed='right'),
           Interval(-2.5172413793103448, -2.413793103448276, closed='right'),
           Interval(-2.413793103448276, -2.310344827586207, closed='right'),
           Interval(-2.310344827586207, -2.206896551724138, closed='right'),
           Interval(-2.206896551724138, -2.103448275862069, closed='right'),
           Interval(-2.103448275862069, -2.0, closed='right')], dtype=object)

  • S2_bins
    (S2_bins)
    object
    (-5.0, -4.896551724137931] ... (...

    long_name :

    $S^2$

    units :

    s$^{-2}$

    array([Interval(-5.0, -4.896551724137931, closed='right'),
           Interval(-4.896551724137931, -4.793103448275862, closed='right'),
           Interval(-4.793103448275862, -4.689655172413793, closed='right'),
           Interval(-4.689655172413793, -4.586206896551724, closed='right'),
           Interval(-4.586206896551724, -4.482758620689655, closed='right'),
           Interval(-4.482758620689655, -4.379310344827586, closed='right'),
           Interval(-4.379310344827586, -4.275862068965517, closed='right'),
           Interval(-4.275862068965517, -4.172413793103448, closed='right'),
           Interval(-4.172413793103448, -4.068965517241379, closed='right'),
           Interval(-4.068965517241379, -3.9655172413793105, closed='right'),
           Interval(-3.9655172413793105, -3.862068965517241, closed='right'),
           Interval(-3.862068965517241, -3.7586206896551726, closed='right'),
           Interval(-3.7586206896551726, -3.655172413793103, closed='right'),
           Interval(-3.655172413793103, -3.5517241379310347, closed='right'),
           Interval(-3.5517241379310347, -3.4482758620689653, closed='right'),
           Interval(-3.4482758620689653, -3.344827586206897, closed='right'),
           Interval(-3.344827586206897, -3.2413793103448274, closed='right'),
           Interval(-3.2413793103448274, -3.137931034482759, closed='right'),
           Interval(-3.137931034482759, -3.0344827586206895, closed='right'),
           Interval(-3.0344827586206895, -2.9310344827586206, closed='right'),
           Interval(-2.9310344827586206, -2.8275862068965516, closed='right'),
           Interval(-2.8275862068965516, -2.7241379310344827, closed='right'),
           Interval(-2.7241379310344827, -2.6206896551724137, closed='right'),
           Interval(-2.6206896551724137, -2.5172413793103448, closed='right'),
           Interval(-2.5172413793103448, -2.413793103448276, closed='right'),
           Interval(-2.413793103448276, -2.310344827586207, closed='right'),
           Interval(-2.310344827586207, -2.206896551724138, closed='right'),
           Interval(-2.206896551724138, -2.103448275862069, closed='right'),
           Interval(-2.103448275862069, -2.0, closed='right')], dtype=object)

  • enso_transition_phase
    (enso_transition_phase)
    object
    'none' 'El-Nino cool' ... 'all'

    description :

    Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

    array(['none', 'El-Nino cool', 'El-Nino warm', 'La-Nina cool', 'La-Nina warm',
           '____________', 'all'], dtype=object)

  • bin_areas
    (N2T_bins, S2_bins)
    float64
    0.0107 0.0107 ... 0.0107 0.0107

    long_name :

    log$_{10} 4N_T^2$

    units :

    s$^{-2}$

    array([[0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
    ...
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155]])

  • Rig_T_bins
    (Rig_T_bins)
    object
    (0.05, 0.1475] ... (1.9025, 2.0]

    long_name :

    $Ri^g_T$

    units :

    array([Interval(0.05, 0.1475, closed='right'),
           Interval(0.1475, 0.245, closed='right'),
           Interval(0.245, 0.3425, closed='right'),
           Interval(0.3425, 0.44, closed='right'),
           Interval(0.44, 0.5375, closed='right'),
           Interval(0.5375, 0.635, closed='right'),
           Interval(0.635, 0.7325, closed='right'),
           Interval(0.7325, 0.83, closed='right'),
           Interval(0.83, 0.9275, closed='right'),
           Interval(0.9275, 1.025, closed='right'),
           Interval(1.025, 1.1225, closed='right'),
           Interval(1.1225, 1.22, closed='right'),
           Interval(1.22, 1.3175, closed='right'),
           Interval(1.3175, 1.415, closed='right'),
           Interval(1.415, 1.5125, closed='right'),
           Interval(1.5125, 1.61, closed='right'),
           Interval(1.61, 1.7075, closed='right'),
           Interval(1.7075, 1.805, closed='right'),
           Interval(1.805, 1.9025, closed='right'),
           Interval(1.9025, 2.0, closed='right')], dtype=object)

  • enso_2phase
    (enso_2phase)
    object
    'El-Nino' 'La-Nina' 'none'

    description :

    Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

    array(['El-Nino', 'La-Nina', 'none'], dtype=object)

  • stat
    (stat)
    <U4
    'mean' 'std'

    array(['mean', 'std'], dtype='<U4')

• Data variables: (29)
  • DFrI_TH
    (depth, time)
    float32
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    Array Chunk Bytes 66.38 MiB 2.29 MiB Shape (100, 174000) (100, 6000) Count 15109 Tasks 29 Chunks Type float32 numpy.ndarray

  • KPPdiffKzT
    (depth, time)
    float32
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    Array Chunk Bytes 66.38 MiB 2.29 MiB Shape (100, 174000) (100, 6000) Count 15109 Tasks 29 Chunks Type float32 numpy.ndarray

  • KPPg_TH
    (depth, time)
    float32
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    Array Chunk Bytes 66.38 MiB 2.29 MiB Shape (100, 174000) (100, 6000) Count 27985 Tasks 29 Chunks Type float32 numpy.ndarray

  • KPPhbl
    (time)
    float32
    dask.array<chunksize=(6000,), meta=np.ndarray>

    Array Chunk Bytes 679.69 kiB 23.44 kiB Shape (174000,) (6000,) Count 14007 Tasks 29 Chunks Type float32 numpy.ndarray

  • KPPviscAz
    (depth, time)
    float32
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    Array Chunk Bytes 66.38 MiB 2.29 MiB Shape (100, 174000) (100, 6000) Count 15109 Tasks 29 Chunks Type float32 numpy.ndarray

  • SSH
    (time)
    float32
    dask.array<chunksize=(6000,), meta=np.ndarray>

    Array Chunk Bytes 679.69 kiB 23.44 kiB Shape (174000,) (6000,) Count 14007 Tasks 29 Chunks Type float32 numpy.ndarray

  • VISrI_Um
    (depth, time)
    float32
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    standard_name :

    ocean_vertical_x_viscosity

    Array Chunk Bytes 66.38 MiB 2.29 MiB Shape (100, 174000) (100, 6000) Count 15109 Tasks 29 Chunks Type float32 numpy.ndarray

  • VISrI_Vm
    (depth, time)
    float32
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    standard_name :

    ocean_vertical_y_viscosity

    Array Chunk Bytes 66.38 MiB 2.29 MiB Shape (100, 174000) (100, 6000) Count 15109 Tasks 29 Chunks Type float32 numpy.ndarray

  • salt
    (depth, time)
    float32
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    Array Chunk Bytes 66.38 MiB 2.29 MiB Shape (100, 174000) (100, 6000) Count 15109 Tasks 29 Chunks Type float32 numpy.ndarray

  • theta
    (depth, time)
    float32
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    standard_name :

    sea_water_potential_temperature

    Array Chunk Bytes 66.38 MiB 2.29 MiB Shape (100, 174000) (100, 6000) Count 15109 Tasks 29 Chunks Type float32 numpy.ndarray

  • u
    (depth, time)
    float32
    0.1444 0.1427 ... -0.2784 -0.2961

    standard_name :

    sea_water_x_velocity

    units :

    m/s

    array([[ 0.14444917,  0.14268853,  0.13952434, ...,  0.02421886,
             0.02243349,  0.02053516],
           [ 0.15630163,  0.15460515,  0.15150057, ...,  0.03685239,
             0.03500494,  0.03318255],
           [ 0.16852501,  0.16686589,  0.1638056 , ...,  0.05514198,
             0.05294416,  0.05094416],
           ...,
           [-0.56134164, -0.5664235 , -0.5761951 , ..., -0.19214931,
            -0.21014069, -0.22925672],
           [-0.59937775, -0.60782665, -0.60269874, ..., -0.22406787,
            -0.24502584, -0.25902018],
           [-0.6396752 , -0.6394655 , -0.62702477, ..., -0.26261088,
            -0.27841273, -0.29612747]], dtype=float32)

  • v
    (depth, time)
    float32
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    standard_name :

    sea_water_y_velocity

    units :

    m/s

    Array Chunk Bytes 66.38 MiB 2.29 MiB Shape (100, 174000) (100, 6000) Count 15109 Tasks 29 Chunks Type float32 numpy.ndarray

  • w
    (depth, time)
    float32
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    Array Chunk Bytes 66.38 MiB 2.29 MiB Shape (100, 174000) (100, 6000) Count 15109 Tasks 29 Chunks Type float32 numpy.ndarray

  • Jq_shear
    (depth, time)
    float64
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    Array Chunk Bytes 132.75 MiB 4.58 MiB Shape (100, 174000) (100, 6000) Count 36874 Tasks 29 Chunks Type float64 numpy.ndarray

  • nonlocal_flux
    (depth, time)
    float64
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    Array Chunk Bytes 132.75 MiB 4.58 MiB Shape (100, 174000) (100, 6000) Count 36874 Tasks 29 Chunks Type float64 numpy.ndarray

  • dens
    (depth, time)
    float32
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    standard_name :

    sea_water_potential_density

    Array Chunk Bytes 66.38 MiB 2.29 MiB Shape (100, 174000) (100, 6000) Count 36599 Tasks 29 Chunks Type float32 numpy.ndarray

  • densT
    (depth, time)
    float32
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    standard_name :

    sea_water_potential_density

    Array Chunk Bytes 66.38 MiB 2.29 MiB Shape (100, 174000) (100, 6000) Count 27987 Tasks 29 Chunks Type float32 numpy.ndarray

  • mld
    (time)
    float32
    dask.array<chunksize=(6000,), meta=np.ndarray>

    long_name :

    MLD

    standard_name :

    mixed_layer_thickness

    units :

    m

    description :

    Interpolate density to 1m grid. Search for max depth where |drho| > 0.015 and N2 > 1e-05

    Array Chunk Bytes 679.69 kiB 23.44 kiB Shape (174000,) (6000,) Count 108462 Tasks 29 Chunks Type float32 numpy.ndarray

  • Jq
    (depth, time)
    float64
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    Array Chunk Bytes 132.75 MiB 4.58 MiB Shape (100, 174000) (100, 6000) Count 71239 Tasks 29 Chunks Type float64 numpy.ndarray

  • dJdz
    (depth, time)
    float64
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    Array Chunk Bytes 132.75 MiB 4.58 MiB Shape (100, 174000) (100, 6000) Count 96991 Tasks 29 Chunks Type float64 numpy.ndarray

  • dTdt
    (depth, time)
    float64
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    long_name :

    $∂T/∂t = -1/(ρ_0c_p) ∂J_q/∂z$

    units :

    °C/month

    Array Chunk Bytes 132.75 MiB 4.58 MiB Shape (100, 174000) (100, 6000) Count 118451 Tasks 29 Chunks Type float64 numpy.ndarray

  • N2T
    (RF, time)
    float64
    2.86e-05 2.929e-05 ... nan nan

    long_name :

    $N_T^2$

    units :

    s$^{-2}$

    array([[2.85961337e-05, 2.92906395e-05, 2.96320558e-05, ...,
            1.72453901e-04, 1.74164901e-04, 1.76380226e-04],
           [2.99647712e-05, 3.03303474e-05, 3.05990664e-05, ...,
            1.05970680e-04, 1.06939710e-04, 1.06192700e-04],
           [3.16447590e-05, 3.18380226e-05, 3.19219229e-05, ...,
            1.13575790e-04, 1.11304373e-04, 1.09663017e-04],
           ...,
           [1.25683483e-04, 1.03881890e-04, 5.09895938e-05, ...,
            6.28577297e-05, 5.75959994e-05, 3.60513162e-05],
           [9.69038030e-05, 4.96963019e-05, 2.72286474e-05, ...,
            7.15225561e-05, 4.38891197e-05, 2.92028869e-05],
           [           nan,            nan,            nan, ...,
                       nan,            nan,            nan]])

  • S2
    (RF, time)
    float32
    nan nan nan nan ... nan nan nan nan

    long_name :

    $S^2$

    units :

    s$^{-2}$

    array([[nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           ...,
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)

  • shred2
    (RF, time)
    float32
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    long_name :

    $Sh_{red}^2$

    units :

    $s^{-2}$

    Array Chunk Bytes 67.04 MiB 2.29 MiB Shape (101, 174000) (100, 6000) Count 152616 Tasks 58 Chunks Type float32 numpy.ndarray

  • Rig_T
    (RF, time)
    float32
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    long_name :

    $Ri^g_T$

    units :

    Array Chunk Bytes 67.04 MiB 2.29 MiB Shape (101, 174000) (100, 6000) Count 149397 Tasks 58 Chunks Type float32 numpy.ndarray

  • eps
    (RF, time)
    float32
    dask.array<chunksize=(100, 6000), meta=np.ndarray>

    long_name :

    $ε$

    units :

    W/kg

    Array Chunk Bytes 67.04 MiB 2.29 MiB Shape (101, 174000) (100, 6000) Count 166592 Tasks 58 Chunks Type float32 numpy.ndarray

  • n2s2pdf
    (N2T_bins, S2_bins, enso_transition_phase)
    float64
    0.001195 0.002036 ... 0.0 0.0

    long_name :

    $P(S^2, 4N_T^2)$

    array([[[1.19524865e-03, 2.03597638e-03, 1.78366820e-03, ...,
             2.94279844e-04, 1.25478442e-03, 1.14875894e-03],
            [1.59366486e-03, 5.34443800e-03, 3.41869737e-03, ...,
             1.96186562e-04, 1.42785813e-03, 1.72313841e-03],
            [1.57469266e-03, 4.07195276e-03, 4.16189246e-03, ...,
             1.96186562e-04, 1.29805284e-03, 1.79071246e-03],
            ...,
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],
    
           [[2.16283089e-03, 4.32644981e-03, 3.27005836e-03, ...,
             1.07902609e-03, 1.99034769e-03, 2.29751788e-03],
            [2.33358069e-03, 3.81745571e-03, 4.16189246e-03, ...,
             1.37330594e-03, 2.12015298e-03, 2.50024004e-03],
            [2.44741390e-03, 6.61692324e-03, 4.16189246e-03, ...,
             2.35423875e-03, 1.77400555e-03, 2.97325843e-03],
    ...
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],
    
           [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            ...,
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]])

  • eps_n2s2
    (N2T_bins, S2_bins, enso_transition_phase)
    float64
    nan -0.0001817 ... nan nan

    long_name :

    $ε$

    units :

    W/kg

    array([[[            nan, -1.81674019e-04,  8.43668265e-04, ...,
              1.85894260e-03,  3.65857477e-04,             nan],
            [            nan,  1.54797663e-05,  1.01844850e-03, ...,
              5.06847088e-02,  2.37010606e-03,             nan],
            [            nan, -1.07046177e-04,  1.98488893e-03, ...,
              8.82282807e-03,  5.05365103e-03,             nan],
            ...,
            [            nan,             nan,             nan, ...,
                         nan,             nan,             nan],
            [            nan,             nan,             nan, ...,
                         nan,             nan,             nan],
            [            nan,             nan,             nan, ...,
                         nan,             nan,             nan]],
    
           [[            nan, -7.94787399e-04,  8.69701218e-04, ...,
              1.84251689e-03,  1.35891405e-03,             nan],
            [            nan,  1.36265098e-05,  1.69439808e-03, ...,
              3.94721083e-03,  1.59124716e-03,             nan],
            [            nan, -9.01015821e-05,  2.06955488e-03, ...,
              7.18349703e-03,  7.39395701e-03,             nan],
    ...
            [            nan,             nan,             nan, ...,
                         nan,             nan,             nan],
            [            nan,             nan,             nan, ...,
                         nan,             nan,             nan],
            [            nan,             nan,             nan, ...,
                         nan,             nan,             nan]],
    
           [[            nan,             nan,             nan, ...,
                         nan,             nan,             nan],
            [            nan,             nan,             nan, ...,
                         nan,             nan,             nan],
            [            nan,             nan,             nan, ...,
                         nan,             nan,             nan],
            ...,
            [            nan,             nan,             nan, ...,
                         nan,             nan,             nan],
            [            nan,             nan,             nan, ...,
                         nan,             nan,             nan],
            [            nan,             nan,             nan, ...,
                         nan,             nan,             nan]]])

  • eps_ri
    (stat, Rig_T_bins, enso_2phase)
    float64
    0.3784 0.5789 ... 0.01693 0.016

    long_name :

    $ε$

    units :

    W/kg

    array([[[ 3.78371483e-01,  5.78936738e-01,  4.64057829e-01],
            [ 5.71774662e-01,  8.88755390e-01,  7.46356632e-01],
            [ 4.65221764e-01,  6.52661260e-01,  5.96713007e-01],
            [ 3.63085428e-01,  4.42385551e-01,  4.30929153e-01],
            [ 3.29458889e-01,  3.31102359e-01,  3.48991588e-01],
            [ 2.58916471e-01,  2.02882638e-01,  2.48407741e-01],
            [ 1.01503633e-01,  5.03806019e-02,  8.61559977e-02],
            [ 2.15781846e-02, -2.56755581e-03,  1.24628026e-02],
            [ 1.26553085e-03, -7.03532485e-03, -2.09370413e-03],
            [-1.53727081e-04, -3.68216083e-03, -1.77202523e-03],
            [ 5.41265441e-04, -1.22245202e-03, -4.25058877e-04],
            [ 1.07155949e-03,  1.02337038e-04,  4.10658414e-04],
            [ 1.19492761e-03,  5.59091397e-04,  7.76343221e-04],
            [ 1.10987443e-03,  8.83374927e-04,  9.07996919e-04],
            [ 1.02902541e-03,  1.03941027e-03,  9.26648063e-04],
            [ 1.04391189e-03,  8.64407664e-04,  8.55321770e-04],
            [ 8.90497193e-04,  9.94021154e-04,  8.66169474e-04],
            [ 7.99575736e-04,  1.02021581e-03,  8.21983852e-04],
            [ 7.60769129e-04,  1.04486402e-03,  8.27765966e-04],
            [ 5.82306426e-04,  1.16837387e-03,  7.66086266e-04]],
    ...
           [[ 7.24344821e-01,  9.70238319e-01,  8.21674369e-01],
            [ 8.41422142e-01,  1.29934842e+00,  1.10142984e+00],
            [ 5.93170946e-01,  7.79707251e-01,  7.33862500e-01],
            [ 4.55758811e-01,  5.05129019e-01,  5.09058654e-01],
            [ 4.63585454e-01,  4.14193245e-01,  4.60824632e-01],
            [ 4.22763933e-01,  3.50425895e-01,  4.08204162e-01],
            [ 2.90278106e-01,  2.25073607e-01,  2.68928815e-01],
            [ 1.40284055e-01,  1.07766743e-01,  1.28812083e-01],
            [ 6.12077402e-02,  5.11335096e-02,  5.91337781e-02],
            [ 3.67457356e-02,  3.24039682e-02,  3.63247977e-02],
            [ 2.92962089e-02,  2.35533987e-02,  2.72322581e-02],
            [ 2.69196956e-02,  2.09546246e-02,  2.38250571e-02],
            [ 2.35301721e-02,  2.04708112e-02,  2.26091269e-02],
            [ 2.13243402e-02,  2.01087417e-02,  2.11583857e-02],
            [ 2.00156098e-02,  2.05699687e-02,  2.05219651e-02],
            [ 2.01522487e-02,  1.75056961e-02,  1.87922994e-02],
            [ 1.81597692e-02,  1.75580519e-02,  1.81476165e-02],
            [ 1.74739817e-02,  1.74928839e-02,  1.75743210e-02],
            [ 1.62677361e-02,  1.58572280e-02,  1.66031149e-02],
            [ 1.39238061e-02,  1.69295883e-02,  1.59981339e-02]]])

• Attributes: (3)

  easting :

  longitude

  northing :

  latitude

  title :

  Station profile, index (i,j)=(1201,240)

mitgcm.u.cf.plot()
mitgcm.mldT.reset_coords(drop=True).cf.plot()
mitgcm.eucmax.reset_coords(drop=True).cf.plot()

KeyboardInterrupt

mixpods.plot_n2s2pdf(mitgcm.n2s2pdf.sel(enso_transition_phase="none"))

<matplotlib.contour.QuadContourSet at 0x2b65e58af010>

MOM6 run : calculate ONI

dirname = "/glade/scratch/dcherian/archive/gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods/ocn/hist"
static = xr.open_dataset(*glob.glob(f"{dirname}/*static*.nc"))
sfc = xr.open_mfdataset(
    sorted(glob.glob(f"{dirname}/*sfc*")),
    coords="minimal",
    data_vars="minimal",
    compat="override",
    use_cftime=True,
    parallel=True,
)
sfc["time"] = sfc.time + datetime.timedelta(days=365*1957)
#sfc["time"] = sfc.time + xr.coding.cftime_offsets.YearBegin(1957)
sfc.coords.update(static.drop("time"))

#sfc["tos"].attrs["coordinates"] = "geolon geolat"
sst = sfc.cf["sea_surface_temperature"]
sst.cf

Coordinates:
- CF Axes: * X: ['xh']
           * Y: ['yh']
           * T: ['time']
             Z: n/a

- CF Coordinates: * longitude: ['xh']
                  * latitude: ['yh']
                  * time: ['time']
                    vertical: n/a

- Cell Measures:   area: ['areacello']
                   volume: n/a

- Standard Names:   cell_area: ['areacello']

- Bounds:   n/a

# Calculate a monthly average sea surface temperature in the Nino 3.4 region (5°S-5°N, 170°W-120°W).
monthly_ = (
    sst.cf.sel(latitude=slice(-5, 5), longitude=slice(-170, -120))
    .cf.mean(["X", "Y"])
    .resample(time="M")
    .mean()
    #.sel(time=slice("1960", "1999-12-31"))
    #.reindex(
    #    time=xr.cftime_range("1955-01-01", monthly.time[-1].dt.strftime("%Y-%m-%d").data.item(), freq="MS")
    #)
    .load()
)

monthly = (
    monthly_
    .reindex(
        time=xr.cftime_range(
            "1956-01-01",
            monthly_.time[-1].dt.strftime("%Y-%m-%d").data.item(), 
            freq="M", 
            calendar=monthly_.time.dt.calendar,
        )
    )
    .convert_calendar("gregorian", use_cftime=False)
)
#monthly = monthly_
monthly.plot()

[<matplotlib.lines.Line2D at 0x2b740fc07280>]

(
    oniobs.hvplot.line(x="time", label="obs")
    * onimom6.hvplot.line(x="time", label="MOM6")
).opts(ylabel="ONI [°C]")

oniobs.enso_transition_mask.plot()
onimom6.enso_transition_mask.plot(color='r')

[<matplotlib.lines.Line2D at 0x2b6d7fd20eb0>]

MOM6 sections

import mom6_tools
import xgcm
from mom6_tools.sections import combine_nested, read_raw_files
from mom6_tools.wright_eos import wright_eos


def combine_variables_by_coords(dsets):
    import itertools

    import tqdm

    all_vars = set(itertools.chain(*[ds.data_vars for ds in dsets]))

    merged = xr.merge(
        xr.combine_by_coords([ds[var] for ds in dsets if ds.sizes["time"] > 0], combine_attrs="override")
        for var in tqdm.tqdm(all_vars)
    )
    
    return merged

import glob

# dirname = "/glade/scratch/gmarques/gmom.e23.GJRAv3.TL319_t061_zstar_N65.mixpods.001/run"

dirname = "/glade/scratch/dcherian/gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods/run/"
files = sorted(glob.glob(f"{dirname}/*TAO*140W*.nc.*"))

lons = [140]  # [90, 110, 140, 155, 170]  # TODO: Add 125
dsets = read_raw_files(files, parallel=True)

combined = combine_variables_by_coords(dsets)

#mom6tao = read_tao_sections(dirname)

100%|██████████| 21/21 [02:36<00:00,  7.44s/it]

combined["time"] = combined["time"] + datetime.timedelta(days=1957*365)

def interp_to_center(ds):
    import toolz as tlz

    ix0 = np.arange(1, ds.sizes["xq"], 4)
    ix1 = ix0 + 1
    indices = list(tlz.interleave([ix0, ix1]))

    # print(ds.xq.data[indices])
    out = (
        ds
        #.isel(xq=[1, 2, 5, 6, 8, 9, 12, 13, 16, 17])
        .isel(xq=[1, 2])
        .coarsen(xq=2).mean()
    )
    out = out.sel(xh = out.xq.data, method="nearest", tolerance=0.05)
    
    for var in out:
        if "xq" in out[var].dims:
            out[var] = out[var].rename({"xq": "xh"}).drop("xh")
    return out.drop_vars("xq")


mom6tao = interp_to_center(combined).cf.chunk({"Y": -1})

mom6tao["time"] = mom6tao.indexes["time"].to_datetimeindex()

mom6tao["dens"] = wright_eos(mom6tao.thetao, mom6tao.so, 0)
mom6tao["dens"].attrs.update(
    {"units": "kg/m^3", "standard_name": "sea_water_potential_density"}
)
mom6tao["densT"] = wright_eos(mom6tao.thetao, 35, 0)
mom6tao["densT"].attrs.update({"standard_name": "sea_water_potential_density", "units": "kg/m3"})


mom6tao

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/coding/times.py:360: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.
  sample = dates.ravel()[0]
/glade/scratch/dcherian/tmp/ipykernel_272468/768503968.py:25: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar.  This may lead to subtle errors in operations that depend on the length of time between dates.
  mom6tao["time"] = mom6tao.indexes["time"].to_datetimeindex()

<xarray.Dataset>
Dimensions:           (yh: 49, time: 506640, xh: 1, zi: 66, nv: 2, zl: 65,
                       yq: 49)
Coordinates:
  * yh                (yh) float64 -5.911 -5.664 -5.417 ... 5.541 5.788 6.035
  * time              (time) datetime64[ns] 1958-01-06T00:30:00 ... 2015-12-3...
  * xh                (xh) float64 -140.0
  * zi                (zi) float64 0.0 2.5 5.0 7.5 ... 5.503e+03 5.751e+03 6e+03
  * nv                (nv) float64 1.0 2.0
  * zl                (zl) float64 1.25 3.75 6.25 ... 5.627e+03 5.876e+03
  * yq                (yq) float64 -6.035 -5.788 -5.541 ... 5.417 5.664 5.911
Data variables: (12/23)
    Kd_heat           (time, zi, yh, xh) float32 dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>
    time_bnds         (time, nv) timedelta64[ns] dask.array<chunksize=(8640, 2), meta=np.ndarray>
    vo                (time, zl, yq, xh) float32 dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>
    tauy              (time, yq, xh) float32 dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>
    Tflx_dia_diff     (time, zi, yh, xh) float32 dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>
    taux              (time, yh, xh) float32 dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>
    ...                ...
    volcello          (time, zl, yh, xh) float32 dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>
    uo                (time, zl, yh, xh) float32 dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>
    thetao            (time, zl, yh, xh) float32 dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>
    SW                (time, yh, xh) float32 dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>
    dens              (time, zl, yh, xh) float32 dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>
    densT             (time, zl, yh, xh) float32 dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • yh: 49
  • time: 506640
  • xh: 1
  • zi: 66
  • nv: 2
  • zl: 65
  • yq: 49
• Coordinates: (7)
  • yh
    (yh)
    float64
    -5.911 -5.664 ... 5.788 6.035

    long_name :

    h point nominal latitude

    units :

    degrees_north

    cartesian_axis :

    Y

    domain_decomposition :

    [210 258 210 221]

    array([-5.911442, -5.664167, -5.416807, -5.16932 , -4.921678, -4.673864,
           -4.425866, -4.177682, -3.929311, -3.680757, -3.432028, -3.183131,
           -2.934077, -2.684877, -2.435542, -2.186084, -1.936516, -1.68685 ,
           -1.437098, -1.187273, -0.937388, -0.687456, -0.437489, -0.187499,
            0.0625  ,  0.312496,  0.562476,  0.812427,  1.062338,  1.312194,
            1.561984,  1.811694,  2.061313,  2.310828,  2.560226,  2.809495,
            3.058623,  3.3076  ,  3.556414,  3.805057,  4.05352 ,  4.301798,
            4.549888,  4.797793,  5.04552 ,  5.293081,  5.540501,  5.787812,
            6.035065])

  • time
    (time)
    datetime64[ns]
    1958-01-06T00:30:00 ... 2015-12-...

    array(['1958-01-06T00:30:00.000000000', '1958-01-06T01:30:00.000000000',
           '1958-01-06T02:30:00.000000000', ..., '2015-12-31T21:30:00.000000000',
           '2015-12-31T22:30:00.000000000', '2015-12-31T23:30:00.000000000'],
          dtype='datetime64[ns]')

  • xh
    (xh)
    float64
    -140.0

    long_name :

    h point nominal longitude

    units :

    degrees_east

    cartesian_axis :

    X

    domain_decomposition :

    [220 222 220 221]

    array([-140.])

  • zi
    (zi)
    float64
    0.0 2.5 5.0 ... 5.751e+03 6e+03

    long_name :

    Interface pseudo-depth, -z*

    units :

    meter

    cartesian_axis :

    Z

    positive :

    down

    array([0.00000e+00, 2.50000e+00, 5.00000e+00, 7.50000e+00, 1.00000e+01,
           1.27700e+01, 1.61500e+01, 2.01600e+01, 2.48100e+01, 3.01000e+01,
           3.60500e+01, 4.26600e+01, 4.99400e+01, 5.79100e+01, 6.65700e+01,
           7.59400e+01, 8.60200e+01, 9.68300e+01, 1.08370e+02, 1.20660e+02,
           1.33720e+02, 1.47570e+02, 1.62260e+02, 1.77850e+02, 1.94410e+02,
           2.12020e+02, 2.30780e+02, 2.50800e+02, 2.72220e+02, 2.95220e+02,
           3.19990e+02, 3.46780e+02, 3.75880e+02, 4.07640e+02, 4.42510e+02,
           4.81010e+02, 5.23800e+02, 5.71700e+02, 6.25710e+02, 6.87080e+02,
           7.57330e+02, 8.38280e+02, 9.32030e+02, 1.04083e+03, 1.16687e+03,
           1.31191e+03, 1.47672e+03, 1.66077e+03, 1.86211e+03, 2.07777e+03,
           2.30441e+03, 2.53891e+03, 2.77875e+03, 3.02206e+03, 3.26758e+03,
           3.51446e+03, 3.76218e+03, 4.01041e+03, 4.25895e+03, 4.50768e+03,
           4.75652e+03, 5.00516e+03, 5.25384e+03, 5.50255e+03, 5.75127e+03,
           6.00000e+03])

  • nv
    (nv)
    float64
    1.0 2.0

    long_name :

    vertex number

    units :

    none

    cartesian_axis :

    N

    array([1., 2.])

  • zl
    (zl)
    float64
    1.25 3.75 ... 5.627e+03 5.876e+03

    long_name :

    Layer pseudo-depth, -z*

    units :

    meter

    cartesian_axis :

    Z

    positive :

    down

    array([1.250000e+00, 3.750000e+00, 6.250000e+00, 8.750000e+00, 1.138500e+01,
           1.446000e+01, 1.815500e+01, 2.248500e+01, 2.745500e+01, 3.307500e+01,
           3.935500e+01, 4.630000e+01, 5.392500e+01, 6.224000e+01, 7.125500e+01,
           8.098000e+01, 9.142500e+01, 1.026000e+02, 1.145150e+02, 1.271900e+02,
           1.406450e+02, 1.549150e+02, 1.700550e+02, 1.861300e+02, 2.032150e+02,
           2.214000e+02, 2.407900e+02, 2.615100e+02, 2.837200e+02, 3.076050e+02,
           3.333850e+02, 3.613300e+02, 3.917600e+02, 4.250750e+02, 4.617600e+02,
           5.024050e+02, 5.477500e+02, 5.987050e+02, 6.563950e+02, 7.222050e+02,
           7.978050e+02, 8.851550e+02, 9.864300e+02, 1.103850e+03, 1.239390e+03,
           1.394315e+03, 1.568745e+03, 1.761440e+03, 1.969940e+03, 2.191090e+03,
           2.421660e+03, 2.658830e+03, 2.900405e+03, 3.144820e+03, 3.391020e+03,
           3.638320e+03, 3.886295e+03, 4.134680e+03, 4.383315e+03, 4.632100e+03,
           4.880840e+03, 5.129500e+03, 5.378195e+03, 5.626910e+03, 5.875635e+03])

  • yq
    (yq)
    float64
    -6.035 -5.788 ... 5.664 5.911

    long_name :

    q point nominal latitude

    units :

    degrees_north

    cartesian_axis :

    Y

    domain_decomposition :

    [209 257 209 221]

    array([-6.035065, -5.787812, -5.540501, -5.293081, -5.04552 , -4.797793,
           -4.549888, -4.301798, -4.05352 , -3.805057, -3.556414, -3.3076  ,
           -3.058623, -2.809495, -2.560226, -2.310828, -2.061313, -1.811694,
           -1.561984, -1.312194, -1.062338, -0.812427, -0.562476, -0.312496,
           -0.0625  ,  0.187499,  0.437489,  0.687456,  0.937388,  1.187273,
            1.437098,  1.68685 ,  1.936516,  2.186084,  2.435542,  2.684877,
            2.934077,  3.183131,  3.432028,  3.680757,  3.929311,  4.177682,
            4.425866,  4.673864,  4.921678,  5.16932 ,  5.416807,  5.664167,
            5.911442])

• Data variables: (23)
  • Kd_heat
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Total diapycnal diffusivity for heat at interfaces

    units :

    m2 s-1

    cell_methods :

    area:mean zi:point yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    Array Chunk Bytes 6.10 GiB 65.50 MiB Shape (506640, 66, 49, 1) (8760, 40, 49, 1) Count 4756 Tasks 116 Chunks Type float32 numpy.ndarray

  • time_bnds
    (time, nv)
    timedelta64[ns]
    dask.array<chunksize=(8640, 2), meta=np.ndarray>

    long_name :

    time axis boundaries

    calendar :

    NOLEAP

    Array Chunk Bytes 7.73 MiB 136.88 kiB Shape (506640, 2) (8760, 2) Count 174 Tasks 58 Chunks Type timedelta64[ns] numpy.ndarray

  • vo
    (time, zl, yq, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Sea Water Y Velocity

    units :

    m s-1

    cell_methods :

    zl:mean yq:point xh:mean time: mean

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    sea_water_y_velocity

    interp_method :

    none

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 4756 Tasks 116 Chunks Type float32 numpy.ndarray

  • tauy
    (time, yq, xh)
    float32
    dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>

    long_name :

    Meridional surface stress ocean interactions with atmos and ice

    units :

    Pa

    cell_methods :

    yq:point xh:mean time: mean

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    surface_downward_y_stress

    interp_method :

    none

    Array Chunk Bytes 94.70 MiB 1.64 MiB Shape (506640, 49, 1) (8760, 49, 1) Count 2610 Tasks 58 Chunks Type float32 numpy.ndarray

  • Tflx_dia_diff
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Diffusive diapycnal temperature flux across interfaces

    units :

    degC m s-1

    cell_methods :

    area:mean zi:point yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    Array Chunk Bytes 6.10 GiB 65.50 MiB Shape (506640, 66, 49, 1) (8760, 40, 49, 1) Count 4756 Tasks 116 Chunks Type float32 numpy.ndarray

  • taux
    (time, yh, xh)
    float32
    dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>

    long_name :

    Zonal surface stress from ocean interactions with atmos and ice

    units :

    Pa

    cell_methods :

    yh:mean xq:point time: mean

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    surface_downward_x_stress

    interp_method :

    none

    Array Chunk Bytes 94.70 MiB 1.64 MiB Shape (506640, 49, 1) (8760, 49, 1) Count 3306 Tasks 58 Chunks Type float32 numpy.ndarray

  • net_heat_surface
    (time, yh, xh)
    float32
    dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>

    long_name :

    Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

    units :

    W m-2

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    surface_downward_heat_flux_in_sea_water

    Array Chunk Bytes 94.70 MiB 1.64 MiB Shape (506640, 49, 1) (8760, 49, 1) Count 2610 Tasks 58 Chunks Type float32 numpy.ndarray

  • h
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Layer Thickness

    units :

    m

    cell_methods :

    area:mean zl:sum yh:mean xh:mean time: mean

    cell_measures :

    volume: volcello area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 4756 Tasks 116 Chunks Type float32 numpy.ndarray

  • average_T1
    (time)
    object
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Start time for average period

    Array Chunk Bytes 3.87 MiB 68.44 kiB Shape (506640,) (8760,) Count 174 Tasks 58 Chunks Type object numpy.ndarray

  • average_DT
    (time)
    timedelta64[ns]
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Length of average period

    Array Chunk Bytes 3.87 MiB 68.44 kiB Shape (506640,) (8760,) Count 174 Tasks 58 Chunks Type timedelta64[ns] numpy.ndarray

  • average_T2
    (time)
    object
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    End time for average period

    Array Chunk Bytes 3.87 MiB 68.44 kiB Shape (506640,) (8760,) Count 174 Tasks 58 Chunks Type object numpy.ndarray

  • zos
    (time, yh, xh)
    float32
    dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>

    long_name :

    Sea surface height above geoid

    units :

    m

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    sea_surface_height_above_geoid

    Array Chunk Bytes 94.70 MiB 1.64 MiB Shape (506640, 49, 1) (8760, 49, 1) Count 2610 Tasks 58 Chunks Type float32 numpy.ndarray

  • so
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Sea Water Salinity

    units :

    psu

    cell_methods :

    area:mean zl:mean yh:mean xh:mean time: mean

    cell_measures :

    volume: volcello area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    sea_water_salinity

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 4756 Tasks 116 Chunks Type float32 numpy.ndarray

  • Kv_u
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Total vertical viscosity at u-points

    units :

    m2 s-1

    cell_methods :

    zl:mean yh:mean xq:point time: mean

    time_avg_info :

    average_T1,average_T2,average_DT

    interp_method :

    none

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 6148 Tasks 116 Chunks Type float32 numpy.ndarray

  • KPP_kheat
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Heat diffusivity due to KPP, as calculated by [CVMix] KPP

    units :

    m2/s

    cell_methods :

    area:mean zi:point yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    Array Chunk Bytes 6.10 GiB 65.50 MiB Shape (506640, 66, 49, 1) (8760, 40, 49, 1) Count 4756 Tasks 116 Chunks Type float32 numpy.ndarray

  • KPP_buoyFlux
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

    units :

    m2/s3

    cell_methods :

    area:mean zi:point yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    Array Chunk Bytes 6.10 GiB 65.50 MiB Shape (506640, 66, 49, 1) (8760, 40, 49, 1) Count 4756 Tasks 116 Chunks Type float32 numpy.ndarray

  • KPP_OBLdepth
    (time, yh, xh)
    float32
    dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>

    long_name :

    Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

    units :

    meter

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    Array Chunk Bytes 94.70 MiB 1.64 MiB Shape (506640, 49, 1) (8760, 49, 1) Count 2610 Tasks 58 Chunks Type float32 numpy.ndarray

  • volcello
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Ocean grid-cell volume

    units :

    m3

    cell_methods :

    area:sum zl:sum yh:sum xh:sum time: mean

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    ocean_volume

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 4756 Tasks 116 Chunks Type float32 numpy.ndarray

  • uo
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Sea Water X Velocity

    units :

    m s-1

    cell_methods :

    zl:mean yh:mean xq:point time: mean

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    sea_water_x_velocity

    interp_method :

    none

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 6148 Tasks 116 Chunks Type float32 numpy.ndarray

  • thetao
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Sea Water Potential Temperature

    units :

    degC

    cell_methods :

    area:mean zl:mean yh:mean xh:mean time: mean

    cell_measures :

    volume: volcello area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    sea_water_potential_temperature

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 4756 Tasks 116 Chunks Type float32 numpy.ndarray

  • SW
    (time, yh, xh)
    float32
    dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>

    long_name :

    Shortwave radiation flux into ocean

    units :

    W m-2

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    net_downward_shortwave_flux_at_sea_water_surface

    Array Chunk Bytes 94.70 MiB 1.64 MiB Shape (506640, 49, 1) (8760, 49, 1) Count 2610 Tasks 58 Chunks Type float32 numpy.ndarray

  • dens
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Sea Water Salinity

    units :

    kg/m^3

    cell_methods :

    area:mean zl:mean yh:mean xh:mean time: mean

    cell_measures :

    volume: volcello area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    sea_water_potential_density

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 12876 Tasks 116 Chunks Type float32 numpy.ndarray

  • densT
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Sea Water Potential Temperature

    units :

    kg/m3

    cell_methods :

    area:mean zl:mean yh:mean xh:mean time: mean

    cell_measures :

    volume: volcello area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    sea_water_potential_density

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 7308 Tasks 116 Chunks Type float32 numpy.ndarray

• Attributes: (0)

Save

mom6tao.drop_vars(["average_DT", "average_T2", "average_T1", "time_bnds"]).chunk({"time": 24*365}).to_zarr(
    f"/glade/scratch/dcherian/archive/gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods/ocn/moorings/tao.zarr",
    mode="w",
    consolidated=True,
)

<xarray.backends.zarr.ZarrStore at 0x2b746511da10>

Read sections

mom6tao = (
    xr.open_dataset(
        "/glade/scratch/dcherian/archive/gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods/ocn/moorings/tao.zarr",
        engine="zarr",
        chunks="auto"
    )
)
# Unfortunately have to do this here so that Grid ios right.
mom6tao = mixpods.normalize_z(mom6tao, sort=True)

if "Kv_v" not in mom6tao:
    warnings.warn("Kv_v not present. Assuming equal to Kv_u")
    mom6tao["Kv_v"] = (mom6tao["vo"].dims, mom6tao.Kv_u.data)
    
mom6tao.Kv_u.attrs["standard_name"] = "ocean_vertical_x_viscosity"
mom6tao.Kv_v.attrs["standard_name"] = "ocean_vertical_y_viscosity"

/glade/scratch/dcherian/tmp/ipykernel_272468/784068456.py:12: UserWarning: Kv_v not present. Assuming equal to Kv_u
  warnings.warn("Kv_v not present. Assuming equal to Kv_u")

grid = xgcm.Grid(
    mom6tao,
    coords={"Z": {"outer": "zi", "center": "zl"}, 
            "X": {"center": "xh"},
            "Y": {"center": "yh", "left": "yq"},
           },
    periodic=False,
    boundary="fill",
    fill_value=np.nan,
    metrics = {("Z",): "h"}, 
)
grid

<xgcm.Grid>
Z Axis (not periodic, boundary='fill'):
  * outer    zi --> center
  * center   zl --> outer
X Axis (not periodic, boundary='fill'):
  * center   xh
Y Axis (not periodic, boundary='fill'):
  * center   yh --> left
  * left     yq --> center

mom6tao = mixpods.prepare(mom6tao, grid, monthly)
mom6tao

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xgcm/grid.py:1471: UserWarning: Metric at ('time', 'zi', 'yh', 'xh') being interpolated from metrics at dimensions ('time', 'zl', 'yh', 'xh'). Boundary value set to 'extend'.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xgcm/grid.py:1471: UserWarning: Metric at ('time', 'zi', 'yh', 'xh') being interpolated from metrics at dimensions ('time', 'zl', 'yh', 'xh'). Boundary value set to 'extend'.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xgcm/grid.py:1471: UserWarning: Metric at ('time', 'zi', 'yq', 'xh') being interpolated from metrics at dimensions ('time', 'zl', 'yh', 'xh'). Boundary value set to 'extend'.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/cf_xarray/accessor.py:1638: UserWarning: Variables {'areacello'} not found in object but are referred to in the CF attributes.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xgcm/grid.py:1471: UserWarning: Metric at ('time', 'zi', 'yh', 'xh') being interpolated from metrics at dimensions ('time', 'zl', 'yh', 'xh'). Boundary value set to 'extend'.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xgcm/grid.py:1471: UserWarning: Metric at ('time', 'zi', 'yq', 'xh') being interpolated from metrics at dimensions ('time', 'zl', 'yh', 'xh'). Boundary value set to 'extend'.
  warnings.warn(

<xarray.Dataset>
Dimensions:           (time: 506640, yh: 49, xh: 1, zi: 66, zl: 65, nv: 2,
                       yq: 49)
Coordinates: (12/13)
  * nv                (nv) float64 1.0 2.0
  * time              (time) datetime64[ns] 1958-01-06T00:30:00 ... 2015-12-3...
  * xh                (xh) float64 -140.0
  * yh                (yh) float64 -5.911 -5.664 -5.417 ... 5.541 5.788 6.035
  * yq                (yq) float64 -6.035 -5.788 -5.541 ... 5.417 5.664 5.911
  * zi                (zi) float64 -6e+03 -5.751e+03 -5.503e+03 ... -2.5 -0.0
    ...                ...
    eucmax            (time, xh) float64 dask.array<chunksize=(8760, 1), meta=np.ndarray>
    mldT              (time, yh, xh) float64 dask.array<chunksize=(8760, 49, 1), meta=np.ndarray>
    oni               (time) float32 nan nan nan nan nan ... nan nan nan nan nan
    warm_mask         (time) bool True True True True ... True True True True
    cool_mask         (time) bool False False False False ... False False False
    enso_transition   (time) <U12 '____________' ... '____________'
Data variables: (12/25)
    KPP_OBLdepth      (time, yh, xh) float32 dask.array<chunksize=(506640, 49, 1), meta=np.ndarray>
    KPP_buoyFlux      (time, zi, yh, xh) float32 dask.array<chunksize=(8760, 26, 49, 1), meta=np.ndarray>
    KPP_kheat         (time, zi, yh, xh) float32 dask.array<chunksize=(8760, 26, 49, 1), meta=np.ndarray>
    Kd_heat           (time, zi, yh, xh) float32 dask.array<chunksize=(8760, 26, 49, 1), meta=np.ndarray>
    Kv_u              (time, zl, yh, xh) float32 dask.array<chunksize=(8760, 25, 49, 1), meta=np.ndarray>
    SW                (time, yh, xh) float32 dask.array<chunksize=(506640, 49, 1), meta=np.ndarray>
    ...                ...
    Kv_v              (time, zl, yq, xh) float32 dask.array<chunksize=(8760, 25, 49, 1), meta=np.ndarray>
    N2T               (time, zi, yh, xh) float32 dask.array<chunksize=(8760, 1, 49, 1), meta=np.ndarray>
    S2                (time, zi, yh, xh) float32 dask.array<chunksize=(8760, 1, 1, 1), meta=np.ndarray>
    shred2            (time, zi, yh, xh) float32 dask.array<chunksize=(8760, 1, 1, 1), meta=np.ndarray>
    Rig_T             (time, zi, yh, xh) float32 dask.array<chunksize=(8760, 1, 1, 1), meta=np.ndarray>
    eps               (time, zi, yh, xh) float32 dask.array<chunksize=(8760, 1, 1, 1), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time: 506640
  • yh: 49
  • xh: 1
  • zi: 66
  • zl: 65
  • nv: 2
  • yq: 49
• Coordinates: (13)
  • nv
    (nv)
    float64
    1.0 2.0

    cartesian_axis :

    N

    long_name :

    vertex number

    units :

    none

    array([1., 2.])

  • time
    (time)
    datetime64[ns]
    1958-01-06T00:30:00 ... 2015-12-...

    array(['1958-01-06T00:30:00.000000000', '1958-01-06T01:30:00.000000000',
           '1958-01-06T02:30:00.000000000', ..., '2015-12-31T21:30:00.000000000',
           '2015-12-31T22:30:00.000000000', '2015-12-31T23:30:00.000000000'],
          dtype='datetime64[ns]')

  • xh
    (xh)
    float64
    -140.0

    cartesian_axis :

    X

    domain_decomposition :

    [220, 222, 220, 221]

    long_name :

    h point nominal longitude

    units :

    degrees_east

    array([-140.])

  • yh
    (yh)
    float64
    -5.911 -5.664 ... 5.788 6.035

    cartesian_axis :

    Y

    domain_decomposition :

    [210, 258, 210, 221]

    long_name :

    h point nominal latitude

    units :

    degrees_north

    array([-5.911442, -5.664167, -5.416807, -5.16932 , -4.921678, -4.673864,
           -4.425866, -4.177682, -3.929311, -3.680757, -3.432028, -3.183131,
           -2.934077, -2.684877, -2.435542, -2.186084, -1.936516, -1.68685 ,
           -1.437098, -1.187273, -0.937388, -0.687456, -0.437489, -0.187499,
            0.0625  ,  0.312496,  0.562476,  0.812427,  1.062338,  1.312194,
            1.561984,  1.811694,  2.061313,  2.310828,  2.560226,  2.809495,
            3.058623,  3.3076  ,  3.556414,  3.805057,  4.05352 ,  4.301798,
            4.549888,  4.797793,  5.04552 ,  5.293081,  5.540501,  5.787812,
            6.035065])

  • yq
    (yq)
    float64
    -6.035 -5.788 ... 5.664 5.911

    cartesian_axis :

    Y

    domain_decomposition :

    [209, 257, 209, 221]

    long_name :

    q point nominal latitude

    units :

    degrees_north

    array([-6.035065, -5.787812, -5.540501, -5.293081, -5.04552 , -4.797793,
           -4.549888, -4.301798, -4.05352 , -3.805057, -3.556414, -3.3076  ,
           -3.058623, -2.809495, -2.560226, -2.310828, -2.061313, -1.811694,
           -1.561984, -1.312194, -1.062338, -0.812427, -0.562476, -0.312496,
           -0.0625  ,  0.187499,  0.437489,  0.687456,  0.937388,  1.187273,
            1.437098,  1.68685 ,  1.936516,  2.186084,  2.435542,  2.684877,
            2.934077,  3.183131,  3.432028,  3.680757,  3.929311,  4.177682,
            4.425866,  4.673864,  4.921678,  5.16932 ,  5.416807,  5.664167,
            5.911442])

  • zi
    (zi)
    float64
    -6e+03 -5.751e+03 ... -2.5 -0.0

    cartesian_axis :

    Z

    long_name :

    Interface pseudo-depth, -z*

    positive :

    up

    units :

    meter

    array([-6.00000e+03, -5.75127e+03, -5.50255e+03, -5.25384e+03, -5.00516e+03,
           -4.75652e+03, -4.50768e+03, -4.25895e+03, -4.01041e+03, -3.76218e+03,
           -3.51446e+03, -3.26758e+03, -3.02206e+03, -2.77875e+03, -2.53891e+03,
           -2.30441e+03, -2.07777e+03, -1.86211e+03, -1.66077e+03, -1.47672e+03,
           -1.31191e+03, -1.16687e+03, -1.04083e+03, -9.32030e+02, -8.38280e+02,
           -7.57330e+02, -6.87080e+02, -6.25710e+02, -5.71700e+02, -5.23800e+02,
           -4.81010e+02, -4.42510e+02, -4.07640e+02, -3.75880e+02, -3.46780e+02,
           -3.19990e+02, -2.95220e+02, -2.72220e+02, -2.50800e+02, -2.30780e+02,
           -2.12020e+02, -1.94410e+02, -1.77850e+02, -1.62260e+02, -1.47570e+02,
           -1.33720e+02, -1.20660e+02, -1.08370e+02, -9.68300e+01, -8.60200e+01,
           -7.59400e+01, -6.65700e+01, -5.79100e+01, -4.99400e+01, -4.26600e+01,
           -3.60500e+01, -3.01000e+01, -2.48100e+01, -2.01600e+01, -1.61500e+01,
           -1.27700e+01, -1.00000e+01, -7.50000e+00, -5.00000e+00, -2.50000e+00,
           -0.00000e+00])

  • zl
    (zl)
    float64
    -5.876e+03 -5.627e+03 ... -1.25

    cartesian_axis :

    Z

    long_name :

    Layer pseudo-depth, -z*

    positive :

    up

    units :

    meter

    array([-5.875635e+03, -5.626910e+03, -5.378195e+03, -5.129500e+03,
           -4.880840e+03, -4.632100e+03, -4.383315e+03, -4.134680e+03,
           -3.886295e+03, -3.638320e+03, -3.391020e+03, -3.144820e+03,
           -2.900405e+03, -2.658830e+03, -2.421660e+03, -2.191090e+03,
           -1.969940e+03, -1.761440e+03, -1.568745e+03, -1.394315e+03,
           -1.239390e+03, -1.103850e+03, -9.864300e+02, -8.851550e+02,
           -7.978050e+02, -7.222050e+02, -6.563950e+02, -5.987050e+02,
           -5.477500e+02, -5.024050e+02, -4.617600e+02, -4.250750e+02,
           -3.917600e+02, -3.613300e+02, -3.333850e+02, -3.076050e+02,
           -2.837200e+02, -2.615100e+02, -2.407900e+02, -2.214000e+02,
           -2.032150e+02, -1.861300e+02, -1.700550e+02, -1.549150e+02,
           -1.406450e+02, -1.271900e+02, -1.145150e+02, -1.026000e+02,
           -9.142500e+01, -8.098000e+01, -7.125500e+01, -6.224000e+01,
           -5.392500e+01, -4.630000e+01, -3.935500e+01, -3.307500e+01,
           -2.745500e+01, -2.248500e+01, -1.815500e+01, -1.446000e+01,
           -1.138500e+01, -8.750000e+00, -6.250000e+00, -3.750000e+00,
           -1.250000e+00])

  • eucmax
    (time, xh)
    float64
    dask.array<chunksize=(8760, 1), meta=np.ndarray>

    units :

    m

    long_name :

    EUC maximum

    positive :

    up

    Array Chunk Bytes 3.87 MiB 68.44 kiB Shape (506640, 1) (8760, 1) Count 1105 Tasks 58 Chunks Type float64 numpy.ndarray

  • mldT
    (time, yh, xh)
    float64
    dask.array<chunksize=(8760, 49, 1), meta=np.ndarray>

    long_name :

    MLD$_θ$

    units :

    m

    description :

    Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

    Array Chunk Bytes 189.40 MiB 3.27 MiB Shape (506640, 49, 1) (8760, 49, 1) Count 1108 Tasks 58 Chunks Type float64 numpy.ndarray

  • oni
    (time)
    float32
    nan nan nan nan ... nan nan nan nan

    long_name :

    ONI

    units :

    degC

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    oceanic_nino_index

    array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)

  • warm_mask
    (time)
    bool
    True True True ... True True True

    array([ True,  True,  True, ...,  True,  True,  True])

  • cool_mask
    (time)
    bool
    False False False ... False False

    array([False, False, False, ..., False, False, False])

  • enso_transition
    (time)
    <U12
    '____________' ... '____________'

    long_name :

    ONI

    units :

    degC

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    oceanic_nino_index

    description :

    Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

    array(['____________', '____________', '____________', ...,
           '____________', '____________', '____________'], dtype='<U12')

• Data variables: (25)
  • KPP_OBLdepth
    (time, yh, xh)
    float32
    dask.array<chunksize=(506640, 49, 1), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    long_name :

    Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    meter

    Array Chunk Bytes 94.70 MiB 94.70 MiB Shape (506640, 49, 1) (506640, 49, 1) Count 6 Tasks 1 Chunks Type float32 numpy.ndarray

  • KPP_buoyFlux
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(8760, 26, 49, 1), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area:mean zi:point yh:mean xh:mean time: mean

    long_name :

    Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m2/s3

    Array Chunk Bytes 6.10 GiB 65.50 MiB Shape (506640, 66, 49, 1) (8760, 40, 49, 1) Count 233 Tasks 116 Chunks Type float32 numpy.ndarray

  • KPP_kheat
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(8760, 26, 49, 1), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area:mean zi:point yh:mean xh:mean time: mean

    long_name :

    Heat diffusivity due to KPP, as calculated by [CVMix] KPP

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m2/s

    Array Chunk Bytes 6.10 GiB 65.50 MiB Shape (506640, 66, 49, 1) (8760, 40, 49, 1) Count 233 Tasks 116 Chunks Type float32 numpy.ndarray

  • Kd_heat
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(8760, 26, 49, 1), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area:mean zi:point yh:mean xh:mean time: mean

    long_name :

    Total diapycnal diffusivity for heat at interfaces

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m2 s-1

    Array Chunk Bytes 6.10 GiB 65.50 MiB Shape (506640, 66, 49, 1) (8760, 40, 49, 1) Count 233 Tasks 116 Chunks Type float32 numpy.ndarray

  • Kv_u
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8760, 25, 49, 1), meta=np.ndarray>

    cell_methods :

    zl:mean yh:mean xq:point time: mean

    interp_method :

    none

    long_name :

    Total vertical viscosity at u-points

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m2 s-1

    standard_name :

    ocean_vertical_x_viscosity

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 233 Tasks 116 Chunks Type float32 numpy.ndarray

  • SW
    (time, yh, xh)
    float32
    dask.array<chunksize=(506640, 49, 1), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    long_name :

    Shortwave radiation flux into ocean

    standard_name :

    net_downward_shortwave_flux_at_sea_water_surface

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    W m-2

    Array Chunk Bytes 94.70 MiB 94.70 MiB Shape (506640, 49, 1) (506640, 49, 1) Count 6 Tasks 1 Chunks Type float32 numpy.ndarray

  • Tflx_dia_diff
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(8760, 26, 49, 1), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area:mean zi:point yh:mean xh:mean time: mean

    long_name :

    Diffusive diapycnal temperature flux across interfaces

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    degC m s-1

    Array Chunk Bytes 6.10 GiB 65.50 MiB Shape (506640, 66, 49, 1) (8760, 40, 49, 1) Count 233 Tasks 116 Chunks Type float32 numpy.ndarray

  • dens
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8760, 25, 49, 1), meta=np.ndarray>

    cell_measures :

    volume: volcello area: areacello

    cell_methods :

    area:mean zl:mean yh:mean xh:mean time: mean

    long_name :

    Sea Water Salinity

    standard_name :

    sea_water_potential_density

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    kg/m^3

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 233 Tasks 116 Chunks Type float32 numpy.ndarray

  • densT
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8760, 25, 49, 1), meta=np.ndarray>

    cell_measures :

    volume: volcello area: areacello

    cell_methods :

    area:mean zl:mean yh:mean xh:mean time: mean

    long_name :

    Sea Water Potential Temperature

    standard_name :

    sea_water_potential_density

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    kg/m3

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 233 Tasks 116 Chunks Type float32 numpy.ndarray

  • h
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8760, 25, 49, 1), meta=np.ndarray>

    cell_measures :

    volume: volcello area: areacello

    cell_methods :

    area:mean zl:sum yh:mean xh:mean time: mean

    long_name :

    Layer Thickness

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 233 Tasks 116 Chunks Type float32 numpy.ndarray

  • net_heat_surface
    (time, yh, xh)
    float32
    dask.array<chunksize=(506640, 49, 1), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    long_name :

    Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

    standard_name :

    surface_downward_heat_flux_in_sea_water

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    W m-2

    Array Chunk Bytes 94.70 MiB 94.70 MiB Shape (506640, 49, 1) (506640, 49, 1) Count 6 Tasks 1 Chunks Type float32 numpy.ndarray

  • so
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8760, 25, 49, 1), meta=np.ndarray>

    cell_measures :

    volume: volcello area: areacello

    cell_methods :

    area:mean zl:mean yh:mean xh:mean time: mean

    long_name :

    Sea Water Salinity

    standard_name :

    sea_water_salinity

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    psu

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 233 Tasks 116 Chunks Type float32 numpy.ndarray

  • taux
    (time, yh, xh)
    float32
    dask.array<chunksize=(506640, 49, 1), meta=np.ndarray>

    cell_methods :

    yh:mean xq:point time: mean

    interp_method :

    none

    long_name :

    Zonal surface stress from ocean interactions with atmos and ice

    standard_name :

    surface_downward_x_stress

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    Pa

    Array Chunk Bytes 94.70 MiB 94.70 MiB Shape (506640, 49, 1) (506640, 49, 1) Count 6 Tasks 1 Chunks Type float32 numpy.ndarray

  • tauy
    (time, yq, xh)
    float32
    dask.array<chunksize=(506640, 49, 1), meta=np.ndarray>

    cell_methods :

    yq:point xh:mean time: mean

    interp_method :

    none

    long_name :

    Meridional surface stress ocean interactions with atmos and ice

    standard_name :

    surface_downward_y_stress

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    Pa

    Array Chunk Bytes 94.70 MiB 94.70 MiB Shape (506640, 49, 1) (506640, 49, 1) Count 6 Tasks 1 Chunks Type float32 numpy.ndarray

  • thetao
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8760, 25, 49, 1), meta=np.ndarray>

    cell_measures :

    volume: volcello area: areacello

    cell_methods :

    area:mean zl:mean yh:mean xh:mean time: mean

    long_name :

    Sea Water Potential Temperature

    standard_name :

    sea_water_potential_temperature

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    degC

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 233 Tasks 116 Chunks Type float32 numpy.ndarray

  • uo
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8760, 25, 49, 1), meta=np.ndarray>

    cell_methods :

    zl:mean yh:mean xq:point time: mean

    interp_method :

    none

    long_name :

    Sea Water X Velocity

    standard_name :

    sea_water_x_velocity

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m s-1

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 233 Tasks 116 Chunks Type float32 numpy.ndarray

  • vo
    (time, zl, yq, xh)
    float32
    dask.array<chunksize=(8760, 25, 49, 1), meta=np.ndarray>

    cell_methods :

    zl:mean yq:point xh:mean time: mean

    interp_method :

    none

    long_name :

    Sea Water Y Velocity

    standard_name :

    sea_water_y_velocity

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m s-1

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 233 Tasks 116 Chunks Type float32 numpy.ndarray

  • volcello
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8760, 25, 49, 1), meta=np.ndarray>

    cell_methods :

    area:sum zl:sum yh:sum xh:sum time: mean

    long_name :

    Ocean grid-cell volume

    standard_name :

    ocean_volume

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m3

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 233 Tasks 116 Chunks Type float32 numpy.ndarray

  • zos
    (time, yh, xh)
    float32
    dask.array<chunksize=(506640, 49, 1), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    long_name :

    Sea surface height above geoid

    standard_name :

    sea_surface_height_above_geoid

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m

    Array Chunk Bytes 94.70 MiB 94.70 MiB Shape (506640, 49, 1) (506640, 49, 1) Count 6 Tasks 1 Chunks Type float32 numpy.ndarray

  • Kv_v
    (time, zl, yq, xh)
    float32
    dask.array<chunksize=(8760, 25, 49, 1), meta=np.ndarray>

    standard_name :

    ocean_vertical_y_viscosity

    Array Chunk Bytes 6.01 GiB 65.50 MiB Shape (506640, 65, 49, 1) (8760, 40, 49, 1) Count 233 Tasks 116 Chunks Type float32 numpy.ndarray

  • N2T
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(8760, 1, 49, 1), meta=np.ndarray>

    long_name :

    $N_T^2$

    units :

    s$^{-2}$

    Array Chunk Bytes 6.10 GiB 63.86 MiB Shape (506640, 66, 49, 1) (8760, 39, 49, 1) Count 5744 Tasks 290 Chunks Type float32 numpy.ndarray

  • S2
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(8760, 1, 1, 1), meta=np.ndarray>

    long_name :

    $S^2$

    units :

    s$^{-2}$

    Array Chunk Bytes 6.10 GiB 61.25 MiB Shape (506640, 66, 49, 1) (8760, 39, 47, 1) Count 22159 Tasks 870 Chunks Type float32 numpy.ndarray

  • shred2
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(8760, 1, 1, 1), meta=np.ndarray>

    long_name :

    $Sh_{red}^2$

    units :

    $s^{-2}$

    Array Chunk Bytes 6.10 GiB 61.25 MiB Shape (506640, 66, 49, 1) (8760, 39, 47, 1) Count 28424 Tasks 870 Chunks Type float32 numpy.ndarray

  • Rig_T
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(8760, 1, 1, 1), meta=np.ndarray>

    long_name :

    $Ri^g_T$

    units :

    Array Chunk Bytes 6.10 GiB 61.25 MiB Shape (506640, 66, 49, 1) (8760, 39, 47, 1) Count 28134 Tasks 870 Chunks Type float32 numpy.ndarray

  • eps
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(8760, 1, 1, 1), meta=np.ndarray>

    long_name :

    $ε$

    units :

    W/kg

    Array Chunk Bytes 6.10 GiB 61.25 MiB Shape (506640, 66, 49, 1) (8760, 39, 47, 1) Count 26974 Tasks 870 Chunks Type float32 numpy.ndarray

• Attributes: (0)

mom6140 = mom6tao.cf.sel(longitude=-140, latitude=0, method="nearest")
mom6140 = mom6140.cf.sel(Z=slice(-250, 0))
mom6140 = mom6140.update(mixpods.pdf_N2S2(mom6140))
mom6140

mom6140 = mixpods.load(mom6140)

mom6140.uo.cf.plot(robust=True)
mom6140.eucmax.cf.plot()
mom6140.mldT.cf.plot(lw=0.5, color='k')

mixpods.plot_n2s2pdf(mom6140.n2s2pdf.sel(enso_transition_phase="none"))

<matplotlib.contour.QuadContourSet at 0x2acdb4c75e10>

Pick simulations

datasets = {"TAO": tao_gridded, "MITgcm": mitgcm, "MOM6": mom6140}

for name, ds in datasets.items():
    (
        ds
        .cf["sea_water_x_velocity"]
        .cf["Z"]
        .plot(marker='.', ls="none", label=name)
    )
plt.legend()

<matplotlib.legend.Legend at 0x2b85772a3f70>

Compare EUC maximum and MLD

mixpods.plot_timeseries(datasets, "mldT", obs="TAO")

datasets["TAO"].eucmax.attrs["long_name"] = "EUC maximum"
mixpods.plot_timeseries(datasets, "eucmax", obs="TAO")

Mean profiles: mean, std

Model mean S2 is lower by a factor of 2

datasets["TAO"].shred2.cf.mean("time").plot()

[<matplotlib.lines.Line2D at 0x2b66ed7b43d0>]

kwargs = dict(show_grid=True, invert_axes=True, legend_position="right", frame_width=300, frame_height=500, 
              ylim=(-1e-3, 1.9e-3), xlim=(-250, 0))

S2 = mixpods.map_hvplot(lambda ds, name: mixpods.plot_profile_fill(ds.S2.load(), name), datasets).opts(**kwargs, ylabel="S^2")
N2 = mixpods.map_hvplot(lambda ds, name: mixpods.plot_profile_fill(4 * ds.N2T.load(), name), datasets).opts(**kwargs, ylabel="4N^2")
Ri = (
    mixpods.map_hvplot(lambda ds, name: mixpods.cfplot(ds.Rig_T.load().median("time"), name), datasets)
    * hv.HLine(0.25).opts(color='k', line_width=0.5)
).opts(**kwargs, ylabel="median Ri_g^T").opts(ylim=(0,1))

(S2 + N2 + Ri)

Remap to EUC coordinate

gcmeq.coords["zeuc"] = gcmeq.depth - gcmeq.eucmax
remapped = flox.xarray.xarray_reduce(
    gcmeq.drop_vars(["SSH", "KPPhbl", "mld", "eucmax"], errors="ignore"),
    "zeuc",
    dim="depth",
    func="mean",
    expected_groups=(np.arange(-250, 250.1, 5),),
    isbin=(True,),
)
remapped["zeuc_bins"].attrs["units"] = "m"
remapped

<xarray.Dataset>
Dimensions:          (time: 174000, longitude: 4, zeuc_bins: 100)
Coordinates:
    latitude         float64 0.025
  * longitude        (longitude) float64 -155.0 -140.0 -125.0 -110.0
  * time             (time) datetime64[ns] 1998-12-31T18:00:00 ... 2018-11-06...
    cool_mask        (time) bool True True True True ... False False False False
    warm_mask        (time) bool False False False False ... True True True True
  * zeuc_bins        (zeuc_bins) object (-250.0, -245.0] ... (245.0, 250.0]
Data variables: (12/25)
    DFrI_TH          (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    KPPdiffKzT       (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    KPPg_TH          (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    KPPviscAz        (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    VISrI_Um         (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    VISrI_Vm         (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    ...               ...
    S2               (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    shear            (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    N2               (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    shred2           (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    Ri               (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    enso_transition  (zeuc_bins, time) <U12 'La-Nina cool' ... 'El-Nino warm'
Attributes:
    easting:   longitude
    northing:  latitude
    title:     Station profile, index (i,j)=(1201,240)

xarray.Dataset

• Dimensions:
  • time: 174000
  • longitude: 4
  • zeuc_bins: 100
• Coordinates: (6)
  • latitude
    ()
    float64
    0.025

    array(0.025)

  • longitude
    (longitude)
    float64
    -155.0 -140.0 -125.0 -110.0

    array([-155.024994, -140.024994, -125.025002, -110.025002])

  • time
    (time)
    datetime64[ns]
    1998-12-31T18:00:00 ... 2018-11-...

    array(['1998-12-31T18:00:00.000000000', '1998-12-31T19:00:00.000000000',
           '1998-12-31T20:00:00.000000000', ..., '2018-11-06T15:00:32.000000000',
           '2018-11-06T16:00:16.000000000', '2018-11-06T17:00:00.000000000'],
          dtype='datetime64[ns]')

  • cool_mask
    (time)
    bool
    True True True ... False False

    array([ True,  True,  True, ..., False, False, False])

  • warm_mask
    (time)
    bool
    False False False ... True True

    array([False, False, False, ...,  True,  True,  True])

  • zeuc_bins
    (zeuc_bins)
    object
    (-250.0, -245.0] ... (245.0, 250.0]

    axis :

    Z

    units :

    m

    array([Interval(-250.0, -245.0, closed='right'),
           Interval(-245.0, -240.0, closed='right'),
           Interval(-240.0, -235.0, closed='right'),
           Interval(-235.0, -230.0, closed='right'),
           Interval(-230.0, -225.0, closed='right'),
           Interval(-225.0, -220.0, closed='right'),
           Interval(-220.0, -215.0, closed='right'),
           Interval(-215.0, -210.0, closed='right'),
           Interval(-210.0, -205.0, closed='right'),
           Interval(-205.0, -200.0, closed='right'),
           Interval(-200.0, -195.0, closed='right'),
           Interval(-195.0, -190.0, closed='right'),
           Interval(-190.0, -185.0, closed='right'),
           Interval(-185.0, -180.0, closed='right'),
           Interval(-180.0, -175.0, closed='right'),
           Interval(-175.0, -170.0, closed='right'),
           Interval(-170.0, -165.0, closed='right'),
           Interval(-165.0, -160.0, closed='right'),
           Interval(-160.0, -155.0, closed='right'),
           Interval(-155.0, -150.0, closed='right'),
           Interval(-150.0, -145.0, closed='right'),
           Interval(-145.0, -140.0, closed='right'),
           Interval(-140.0, -135.0, closed='right'),
           Interval(-135.0, -130.0, closed='right'),
           Interval(-130.0, -125.0, closed='right'),
           Interval(-125.0, -120.0, closed='right'),
           Interval(-120.0, -115.0, closed='right'),
           Interval(-115.0, -110.0, closed='right'),
           Interval(-110.0, -105.0, closed='right'),
           Interval(-105.0, -100.0, closed='right'),
           Interval(-100.0, -95.0, closed='right'),
           Interval(-95.0, -90.0, closed='right'),
           Interval(-90.0, -85.0, closed='right'),
           Interval(-85.0, -80.0, closed='right'),
           Interval(-80.0, -75.0, closed='right'),
           Interval(-75.0, -70.0, closed='right'),
           Interval(-70.0, -65.0, closed='right'),
           Interval(-65.0, -60.0, closed='right'),
           Interval(-60.0, -55.0, closed='right'),
           Interval(-55.0, -50.0, closed='right'),
           Interval(-50.0, -45.0, closed='right'),
           Interval(-45.0, -40.0, closed='right'),
           Interval(-40.0, -35.0, closed='right'),
           Interval(-35.0, -30.0, closed='right'),
           Interval(-30.0, -25.0, closed='right'),
           Interval(-25.0, -20.0, closed='right'),
           Interval(-20.0, -15.0, closed='right'),
           Interval(-15.0, -10.0, closed='right'),
           Interval(-10.0, -5.0, closed='right'),
           Interval(-5.0, 0.0, closed='right'), Interval(0.0, 5.0, closed='right'),
           Interval(5.0, 10.0, closed='right'),
           Interval(10.0, 15.0, closed='right'),
           Interval(15.0, 20.0, closed='right'),
           Interval(20.0, 25.0, closed='right'),
           Interval(25.0, 30.0, closed='right'),
           Interval(30.0, 35.0, closed='right'),
           Interval(35.0, 40.0, closed='right'),
           Interval(40.0, 45.0, closed='right'),
           Interval(45.0, 50.0, closed='right'),
           Interval(50.0, 55.0, closed='right'),
           Interval(55.0, 60.0, closed='right'),
           Interval(60.0, 65.0, closed='right'),
           Interval(65.0, 70.0, closed='right'),
           Interval(70.0, 75.0, closed='right'),
           Interval(75.0, 80.0, closed='right'),
           Interval(80.0, 85.0, closed='right'),
           Interval(85.0, 90.0, closed='right'),
           Interval(90.0, 95.0, closed='right'),
           Interval(95.0, 100.0, closed='right'),
           Interval(100.0, 105.0, closed='right'),
           Interval(105.0, 110.0, closed='right'),
           Interval(110.0, 115.0, closed='right'),
           Interval(115.0, 120.0, closed='right'),
           Interval(120.0, 125.0, closed='right'),
           Interval(125.0, 130.0, closed='right'),
           Interval(130.0, 135.0, closed='right'),
           Interval(135.0, 140.0, closed='right'),
           Interval(140.0, 145.0, closed='right'),
           Interval(145.0, 150.0, closed='right'),
           Interval(150.0, 155.0, closed='right'),
           Interval(155.0, 160.0, closed='right'),
           Interval(160.0, 165.0, closed='right'),
           Interval(165.0, 170.0, closed='right'),
           Interval(170.0, 175.0, closed='right'),
           Interval(175.0, 180.0, closed='right'),
           Interval(180.0, 185.0, closed='right'),
           Interval(185.0, 190.0, closed='right'),
           Interval(190.0, 195.0, closed='right'),
           Interval(195.0, 200.0, closed='right'),
           Interval(200.0, 205.0, closed='right'),
           Interval(205.0, 210.0, closed='right'),
           Interval(210.0, 215.0, closed='right'),
           Interval(215.0, 220.0, closed='right'),
           Interval(220.0, 225.0, closed='right'),
           Interval(225.0, 230.0, closed='right'),
           Interval(230.0, 235.0, closed='right'),
           Interval(235.0, 240.0, closed='right'),
           Interval(240.0, 245.0, closed='right'),
           Interval(245.0, 250.0, closed='right')], dtype=object)

• Data variables: (25)
  • DFrI_TH
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • KPPdiffKzT
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • KPPg_TH
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 22301 Tasks 116 Chunks Type float32 numpy.ndarray

  • KPPviscAz
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • VISrI_Um
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • VISrI_Vm
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • salt
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • theta
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • u
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • v
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • w
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • Jq_shear
    (time, longitude, zeuc_bins)
    float64
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 531.01 MiB 4.58 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 31190 Tasks 116 Chunks Type float64 numpy.ndarray

  • nonlocal_flux
    (time, longitude, zeuc_bins)
    float64
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 531.01 MiB 4.58 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 31190 Tasks 116 Chunks Type float64 numpy.ndarray

  • dens
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 30915 Tasks 116 Chunks Type float32 numpy.ndarray

  • Jq
    (time, longitude, zeuc_bins)
    float64
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 531.01 MiB 4.58 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 65555 Tasks 116 Chunks Type float64 numpy.ndarray

  • dJdz
    (time, longitude, zeuc_bins)
    float64
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 531.01 MiB 4.58 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 91307 Tasks 116 Chunks Type float64 numpy.ndarray

  • dTdt
    (time, longitude, zeuc_bins)
    float64
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    long_name :

    $∂T/∂t = -1/(ρ_0c_p) ∂J_q/∂z$

    units :

    °C/month

    Array Chunk Bytes 531.01 MiB 4.58 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 112767 Tasks 116 Chunks Type float64 numpy.ndarray

  • uz
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 10121 Tasks 116 Chunks Type float32 numpy.ndarray

  • vz
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 10121 Tasks 116 Chunks Type float32 numpy.ndarray

  • S2
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    long_name :

    $S^2$

    units :

    s$^{-2}$

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 20010 Tasks 116 Chunks Type float32 numpy.ndarray

  • shear
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    long_name :

    |$u_z$|

    units :

    s$^{-1}$

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 20126 Tasks 116 Chunks Type float32 numpy.ndarray

  • N2
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    long_name :

    $N^2$

    units :

    s$^{-2}$

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 31727 Tasks 116 Chunks Type float32 numpy.ndarray

  • shred2
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    long_name :

    Reduced shear$^2$

    units :

    $s^{-2}$

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 51389 Tasks 116 Chunks Type float32 numpy.ndarray

  • Ri
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    long_name :

    Ri

    units :

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 51273 Tasks 116 Chunks Type float32 numpy.ndarray

  • enso_transition
    (zeuc_bins, time)
    <U12
    'La-Nina cool' ... 'El-Nino warm'

    description :

    Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

    array([['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ...,
            'El-Nino warm', 'El-Nino warm', 'El-Nino warm'],
           ['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ...,
            'El-Nino warm', 'El-Nino warm', 'El-Nino warm'],
           ['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ...,
            'El-Nino warm', 'El-Nino warm', 'El-Nino warm'],
           ...,
           ['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ...,
            'El-Nino warm', 'El-Nino warm', 'El-Nino warm'],
           ['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ...,
            'El-Nino warm', 'El-Nino warm', 'El-Nino warm'],
           ['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ...,
            'El-Nino warm', 'El-Nino warm', 'El-Nino warm']], dtype='<U12')

• Attributes: (3)

  easting :

  longitude

  northing :

  latitude

  title :

  Station profile, index (i,j)=(1201,240)

remapped = remapped.persist()

cluster.scale(6)

Seasonal median Ri: (0, 140)

remapped["longitude"] = [-155.0, -140, -125, -110]

fg = (
    remapped.Ri.groupby("time.season").median()
    .sel(season=["DJF", "MAM", "JJA", "SON"], longitude=[-140, -110])
    .cf.plot(col="season", row="longitude", xlim=(0,1.5), ylim=(-20, None), label="MITgcm")
)
fg.data = tao_Ri.cf.sel(quantile=0.5, longitude=[-140, -110])
fg.map_dataarray_line(xr.plot.line, x=None, y="zeuc", hue="season", color='k', label="TAO")
fg.map(lambda: plt.axvline(0.25))
fg.axes[-1, -1].legend(bbox_to_anchor=(1.5, 1))

<matplotlib.legend.Legend at 0x2add93b58220>

Stability diagram: 4N2-S2 plane

Warner & Moum (2019):

Both velocity and temperature are hourly averaged before interpolation to 5-m vertical bins

Contours enclose 50% of data

mixpods.plot_stability_diagram_by_dataset(datasets)

Questions:

1. internal wave spectrum?

2. minor axis is smaller for model, particularly MITgcm-> too diffusive?

3. Can we think of this as the model relaxing to critical Ri too quickly

mixpods.plot_stability_diagram_by_phase(datasets, obs="TAO", fig=None)

Composed

excluding MLD

• have not matched vertical grid spacing yet.

• minor axis is smaller for increasing shear with KPP

• prandtl number

• interpolate to TAO grid

• hybrid HYCOM-like case may have equatorial refinement of vertical grid above EUC

• compare hybrid coordinate case resolution

fig = plt.figure(constrained_layout=True, figsize=(12, 6))
subfigs = fig.subfigures(2, 1, height_ratios=[1.2, 1])
top = subfigs[0].subfigures(1, 3, width_ratios=[1, 5, 1])

mixpods.plot_stability_diagram_by_dataset(datasets, fig=top[1])
mixpods.plot_stability_diagram_by_phase(datasets, fig=subfigs[1])

including MLD

fig = plt.figure(constrained_layout=True, figsize=(12, 6))
subfigs = fig.subfigures(2, 1, height_ratios=[1.2, 1])
top = subfigs[0].subfigures(1, 3, width_ratios=[1, 5, 1])

mixpods.plot_stability_diagram_by_dataset(datasets, fig=top[1])
mixpods.plot_stability_diagram_by_phase(datasets, fig=subfigs[1])

(   
    tao_gridded.n2s2pdf
    .sel(enso_transition_phase=["La-Nina cool", "La-Nina warm", "El-Nino warm", "El-Nino cool"])
    .sum("N2_bins")
    .plot(hue="enso_transition_phase")
)
plt.figure()
(
    tao_gridded.n2s2pdf
    .sel(enso_transition_phase=["La-Nina cool", "La-Nina warm", "El-Nino warm", "El-Nino cool"])
    .sum("S2_bins")
    .plot(hue="enso_transition_phase")
)

[<matplotlib.lines.Line2D at 0x2b36e2bc2ad0>,
 <matplotlib.lines.Line2D at 0x2b36e2bc32e0>,
 <matplotlib.lines.Line2D at 0x2b36e2bc3310>,
 <matplotlib.lines.Line2D at 0x2b36e0ca52d0>]

oni = pump.obs.process_oni().sel(time=slice("2005-Oct", "2015"))
(
    oni.hvplot.step(color='k')
+ pump.obs.make_enso_transition_mask().sel(time=slice("2005-Jun", "2015")).reset_coords(drop=True).hvplot.step()
).cols(1)

ε

tao_gridded.

<xarray.Dataset>
Dimensions:                (time: 212302, depth: 61, depthchi: 7, N2T_bins: 29,
                            S2_bins: 29, enso_transition_phase: 7,
                            Rig_T_bins: 20, enso_2phase: 3, stat: 2)
Coordinates: (12/23)
    deepest                (time) float64 -200.0 -200.0 -200.0 ... nan nan nan
  * depth                  (depth) float64 -300.0 -295.0 -290.0 ... -5.0 0.0
    eucmax                 (time) float64 -25.0 -25.0 -25.0 ... -85.0 -80.0
    latitude               float32 0.0
    longitude              float32 -140.0
    mld                    (time) float64 0.0 0.0 0.0 0.0 ... nan nan nan nan
    ...                     ...
  * S2_bins                (S2_bins) object (-5.0, -4.896551724137931] ... (-...
  * enso_transition_phase  (enso_transition_phase) object 'none' ... 'all'
    bin_areas              (N2T_bins, S2_bins) float64 0.0107 0.0107 ... 0.0107
  * Rig_T_bins             (Rig_T_bins) object (0.05, 0.1475] ... (1.9025, 2.0]
  * enso_2phase            (enso_2phase) object 'El-Nino' 'La-Nina' 'none'
  * stat                   (stat) <U4 'mean' 'std'
Data variables: (12/20)
    N2                     (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
    N2T                    (time, depth) float64 nan nan nan nan ... nan nan nan
    Ri                     (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
    Rig_T                  (time, depth) float64 nan nan nan nan ... nan nan nan
    S                      (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
    S2                     (time, depth) float32 nan nan nan nan ... nan nan nan
    ...                     ...
    eps                    (depthchi, time) float64 dask.array<chunksize=(7, 95277), meta=np.ndarray>
    Jq                     (depthchi, time) float64 dask.array<chunksize=(7, 95277), meta=np.ndarray>
    shred2                 (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
    n2s2pdf                (N2T_bins, S2_bins, enso_transition_phase) float64 ...
    eps_n2s2               (N2T_bins, S2_bins, enso_transition_phase) float64 ...
    eps_ri                 (stat, Rig_T_bins, enso_2phase) float64 6.847e-08 ...

xarray.Dataset

• Dimensions:
  • time: 212302
  • depth: 61
  • depthchi: 7
  • N2T_bins: 29
  • S2_bins: 29
  • enso_transition_phase: 7
  • Rig_T_bins: 20
  • enso_2phase: 3
  • stat: 2
• Coordinates: (23)
  • deepest
    (time)
    float64
    -200.0 -200.0 -200.0 ... nan nan

    description :

    Deepest depth with a valid observation

    units :

    m

    array([-200., -200., -200., ...,   nan,   nan,   nan])

  • depth
    (depth)
    float64
    -300.0 -295.0 -290.0 ... -5.0 0.0

    axis :

    Z

    positive :

    up

    units :

    m

    array([-300., -295., -290., -285., -280., -275., -270., -265., -260., -255.,
           -250., -245., -240., -235., -230., -225., -220., -215., -210., -205.,
           -200., -195., -190., -185., -180., -175., -170., -165., -160., -155.,
           -150., -145., -140., -135., -130., -125., -120., -115., -110., -105.,
           -100.,  -95.,  -90.,  -85.,  -80.,  -75.,  -70.,  -65.,  -60.,  -55.,
            -50.,  -45.,  -40.,  -35.,  -30.,  -25.,  -20.,  -15.,  -10.,   -5.,
              0.])

  • eucmax
    (time)
    float64
    -25.0 -25.0 -25.0 ... -85.0 -80.0

    units :

    m

    long_name :

    EUC maximum

    positive :

    up

    array([-25., -25., -25., ..., -85., -85., -80.])

  • latitude
    ()
    float32
    0.0

    array(0., dtype=float32)

  • longitude
    ()
    float32
    -140.0

    array(-140., dtype=float32)

  • mld
    (time)
    float64
    0.0 0.0 0.0 0.0 ... nan nan nan nan

    long_name :

    $z_{MLD}$

    units :

    m

    array([ 0.,  0.,  0., ..., nan, nan, nan])

  • mldT
    (time)
    float64
    -1.0 -1.0 -1.0 -1.0 ... nan nan nan

    long_name :

    MLD$_θ$

    units :

    m

    description :

    Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

    array([-1., -1., -1., ..., nan, nan, nan])

  • reference_pressure
    ()
    int64
    0

    array(0)

  • shallowest
    (time)
    float64
    0.0 0.0 0.0 0.0 ... nan nan nan nan

    array([ 0.,  0.,  0., ..., nan, nan, nan])

  • time
    (time)
    datetime64[ns]
    1996-01-01 ... 2020-03-20T21:00:00

    array(['1996-01-01T00:00:00.000000000', '1996-01-01T01:00:00.000000000',
           '1996-01-01T02:00:00.000000000', ..., '2020-03-20T19:00:00.000000000',
           '2020-03-20T20:00:00.000000000', '2020-03-20T21:00:00.000000000'],
          dtype='datetime64[ns]')

  • zeuc
    (depth, time)
    float64
    nan nan nan nan ... nan nan nan nan

    axis :

    Z

    positive :

    up

    units :

    m

    array([[nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           ...,
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan]])

  • depthchi
    (depthchi)
    float64
    -119.0 -89.0 -69.0 ... -39.0 -29.0

    long_name :

    depths of sensors along the mooring line

    standard_name :

    depth

    units :

    m

    axis :

    Z

    positive :

    up

    valid_min :

    29.0

    valid_max :

    119.0

    array([-119.,  -89.,  -69.,  -59.,  -49.,  -39.,  -29.])

  • index
    (time)
    float32
    -0.98 -0.98 -0.98 ... nan nan nan

    array([-0.98, -0.98, -0.98, ...,   nan,   nan,   nan], dtype=float32)

  • warm_mask
    (time)
    bool
    True True True ... True True True

    array([ True,  True,  True, ...,  True,  True,  True])

  • cool_mask
    (time)
    bool
    False False False ... False False

    array([False, False, False, ..., False, False, False])

  • enso_transition
    (time)
    <U12
    'La-Nina warm' ... '____________'

    description :

    Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

    array(['La-Nina warm', 'La-Nina warm', 'La-Nina warm', ...,
           '____________', '____________', '____________'], dtype='<U12')

  • N2T_bins
    (N2T_bins)
    object
    (-5.0, -4.896551724137931] ... (...

    long_name :

    $N_T²$

    array([Interval(-5.0, -4.896551724137931, closed='right'),
           Interval(-4.896551724137931, -4.793103448275862, closed='right'),
           Interval(-4.793103448275862, -4.689655172413793, closed='right'),
           Interval(-4.689655172413793, -4.586206896551724, closed='right'),
           Interval(-4.586206896551724, -4.482758620689655, closed='right'),
           Interval(-4.482758620689655, -4.379310344827586, closed='right'),
           Interval(-4.379310344827586, -4.275862068965517, closed='right'),
           Interval(-4.275862068965517, -4.172413793103448, closed='right'),
           Interval(-4.172413793103448, -4.068965517241379, closed='right'),
           Interval(-4.068965517241379, -3.9655172413793105, closed='right'),
           Interval(-3.9655172413793105, -3.862068965517241, closed='right'),
           Interval(-3.862068965517241, -3.7586206896551726, closed='right'),
           Interval(-3.7586206896551726, -3.655172413793103, closed='right'),
           Interval(-3.655172413793103, -3.5517241379310347, closed='right'),
           Interval(-3.5517241379310347, -3.4482758620689653, closed='right'),
           Interval(-3.4482758620689653, -3.344827586206897, closed='right'),
           Interval(-3.344827586206897, -3.2413793103448274, closed='right'),
           Interval(-3.2413793103448274, -3.137931034482759, closed='right'),
           Interval(-3.137931034482759, -3.0344827586206895, closed='right'),
           Interval(-3.0344827586206895, -2.9310344827586206, closed='right'),
           Interval(-2.9310344827586206, -2.8275862068965516, closed='right'),
           Interval(-2.8275862068965516, -2.7241379310344827, closed='right'),
           Interval(-2.7241379310344827, -2.6206896551724137, closed='right'),
           Interval(-2.6206896551724137, -2.5172413793103448, closed='right'),
           Interval(-2.5172413793103448, -2.413793103448276, closed='right'),
           Interval(-2.413793103448276, -2.310344827586207, closed='right'),
           Interval(-2.310344827586207, -2.206896551724138, closed='right'),
           Interval(-2.206896551724138, -2.103448275862069, closed='right'),
           Interval(-2.103448275862069, -2.0, closed='right')], dtype=object)

  • S2_bins
    (S2_bins)
    object
    (-5.0, -4.896551724137931] ... (...

    long_name :

    $S²$

    array([Interval(-5.0, -4.896551724137931, closed='right'),
           Interval(-4.896551724137931, -4.793103448275862, closed='right'),
           Interval(-4.793103448275862, -4.689655172413793, closed='right'),
           Interval(-4.689655172413793, -4.586206896551724, closed='right'),
           Interval(-4.586206896551724, -4.482758620689655, closed='right'),
           Interval(-4.482758620689655, -4.379310344827586, closed='right'),
           Interval(-4.379310344827586, -4.275862068965517, closed='right'),
           Interval(-4.275862068965517, -4.172413793103448, closed='right'),
           Interval(-4.172413793103448, -4.068965517241379, closed='right'),
           Interval(-4.068965517241379, -3.9655172413793105, closed='right'),
           Interval(-3.9655172413793105, -3.862068965517241, closed='right'),
           Interval(-3.862068965517241, -3.7586206896551726, closed='right'),
           Interval(-3.7586206896551726, -3.655172413793103, closed='right'),
           Interval(-3.655172413793103, -3.5517241379310347, closed='right'),
           Interval(-3.5517241379310347, -3.4482758620689653, closed='right'),
           Interval(-3.4482758620689653, -3.344827586206897, closed='right'),
           Interval(-3.344827586206897, -3.2413793103448274, closed='right'),
           Interval(-3.2413793103448274, -3.137931034482759, closed='right'),
           Interval(-3.137931034482759, -3.0344827586206895, closed='right'),
           Interval(-3.0344827586206895, -2.9310344827586206, closed='right'),
           Interval(-2.9310344827586206, -2.8275862068965516, closed='right'),
           Interval(-2.8275862068965516, -2.7241379310344827, closed='right'),
           Interval(-2.7241379310344827, -2.6206896551724137, closed='right'),
           Interval(-2.6206896551724137, -2.5172413793103448, closed='right'),
           Interval(-2.5172413793103448, -2.413793103448276, closed='right'),
           Interval(-2.413793103448276, -2.310344827586207, closed='right'),
           Interval(-2.310344827586207, -2.206896551724138, closed='right'),
           Interval(-2.206896551724138, -2.103448275862069, closed='right'),
           Interval(-2.103448275862069, -2.0, closed='right')], dtype=object)

  • enso_transition_phase
    (enso_transition_phase)
    object
    'none' 'El-Nino cool' ... 'all'

    description :

    Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

    array(['none', 'El-Nino cool', 'El-Nino warm', 'La-Nina cool', 'La-Nina warm',
           '____________', 'all'], dtype=object)

  • bin_areas
    (N2T_bins, S2_bins)
    float64
    0.0107 0.0107 ... 0.0107 0.0107

    long_name :

    log$_{10} 4N_T^2$

    array([[0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
    ...
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155]])

  • Rig_T_bins
    (Rig_T_bins)
    object
    (0.05, 0.1475] ... (1.9025, 2.0]

    long_name :

    $Ri^g_T$

    units :

    array([Interval(0.05, 0.1475, closed='right'),
           Interval(0.1475, 0.245, closed='right'),
           Interval(0.245, 0.3425, closed='right'),
           Interval(0.3425, 0.44, closed='right'),
           Interval(0.44, 0.5375, closed='right'),
           Interval(0.5375, 0.635, closed='right'),
           Interval(0.635, 0.7325, closed='right'),
           Interval(0.7325, 0.83, closed='right'),
           Interval(0.83, 0.9275, closed='right'),
           Interval(0.9275, 1.025, closed='right'),
           Interval(1.025, 1.1225, closed='right'),
           Interval(1.1225, 1.22, closed='right'),
           Interval(1.22, 1.3175, closed='right'),
           Interval(1.3175, 1.415, closed='right'),
           Interval(1.415, 1.5125, closed='right'),
           Interval(1.5125, 1.61, closed='right'),
           Interval(1.61, 1.7075, closed='right'),
           Interval(1.7075, 1.805, closed='right'),
           Interval(1.805, 1.9025, closed='right'),
           Interval(1.9025, 2.0, closed='right')], dtype=object)

  • enso_2phase
    (enso_2phase)
    object
    'El-Nino' 'La-Nina' 'none'

    description :

    Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

    array(['El-Nino', 'La-Nina', 'none'], dtype=object)

  • stat
    (stat)
    <U4
    'mean' 'std'

    array(['mean', 'std'], dtype='<U4')

• Data variables: (20)
  • N2
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    long_name :

    $N²$

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Count 19 Tasks 3 Chunks Type float64 numpy.ndarray

  • N2T
    (time, depth)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    $N_T²$

    array([[       nan,        nan,        nan, ..., 0.00044498, 0.00130623,
            0.00177282],
           [       nan,        nan,        nan, ..., 0.0004469 , 0.00129765,
            0.00175842],
           [       nan,        nan,        nan, ..., 0.00045489, 0.00128047,
            0.0017273 ],
           ...,
           [       nan,        nan,        nan, ...,        nan,        nan,
                   nan],
           [       nan,        nan,        nan, ...,        nan,        nan,
                   nan],
           [       nan,        nan,        nan, ...,        nan,        nan,
                   nan]])

  • Ri
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    long_name :

    $Ri_g$

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Count 19 Tasks 3 Chunks Type float64 numpy.ndarray

  • Rig_T
    (time, depth)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    $Ri^g_T$

    units :

    array([[nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           ...,
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan]])

  • S
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    41

    generic_name :

    sal

    long_name :

    SALINITY (PSU)

    name :

    S

    standard_name :

    sea_water_salinity

    units :

    PSU

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Count 19 Tasks 3 Chunks Type float64 numpy.ndarray

  • S2
    (time, depth)
    float32
    nan nan nan nan ... nan nan nan nan

    long_name :

    $S²$

    array([[nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           ...,
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)

  • T
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    FORTRAN_format :

    f10.2

    epic_code :

    20

    generic_name :

    temp

    long_name :

    TEMPERATURE (C)

    name :

    T

    standard_name :

    sea_water_temperature

    units :

    C

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Count 19 Tasks 3 Chunks Type float64 numpy.ndarray

  • dens
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    long_name :

    $ρ$

    standard_name :

    sea_water_potential_density

    units :

    kg/m3

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Count 19 Tasks 3 Chunks Type float64 numpy.ndarray

  • densT
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    description :

    density using T, S

    long_name :

    $ρ_T$

    standard_name :

    sea_water_potential_density

    units :

    kg/m3

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Count 19 Tasks 3 Chunks Type float64 numpy.ndarray

  • theta
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    description :

    potential temperature using T, S=35

    long_name :

    $θ$

    standard_name :

    sea_water_potential_temperature

    units :

    degC

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Count 19 Tasks 3 Chunks Type float64 numpy.ndarray

  • u
    (time, depth)
    float32
    nan nan nan nan ... nan nan nan nan

    FORTRAN_format :

    epic_code :

    1205

    generic_name :

    u

    long_name :

    u

    name :

    u

    standard_name :

    sea_water_x_velocity

    units :

    m/s

    array([[nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           ...,
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)

  • v
    (time, depth)
    float32
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    1206

    generic_name :

    v

    long_name :

    v

    name :

    v

    standard_name :

    sea_water_y_velocity

    units :

    m/s

    Array Chunk Bytes 49.40 MiB 23.27 MiB Shape (212302, 61) (100000, 61) Count 10 Tasks 3 Chunks Type float32 numpy.ndarray

  • chi
    (depthchi, time)
    float64
    dask.array<chunksize=(7, 95277), meta=np.ndarray>

    long_name :

    dissipation of temperature variance

    units :

    K2 s-1

    FillValue :

    -9999

    valid_min :

    1.0000000000000001e-11

    valid_max :

    0.0001

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    source :

    inferred from fast thermistor spectral scaling

    references :

    Moum J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 11.34 MiB 5.09 MiB Shape (7, 212302) (7, 95277) Count 78 Tasks 11 Chunks Type float64 numpy.ndarray

  • KT
    (depthchi, time)
    float64
    dask.array<chunksize=(7, 95277), meta=np.ndarray>

    long_name :

    turbulent diffusivity of temperature

    standard_name :

    ocean_vertical_heat_diffusivity

    units :

    m2 s-1

    FillValue :

    -9999

    valid_min :

    1e-10

    valid_max :

    0.1

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    source :

    inferred from fast thermistor spectral scaling

    references :

    Moum, J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 11.34 MiB 5.09 MiB Shape (7, 212302) (7, 95277) Count 78 Tasks 11 Chunks Type float64 numpy.ndarray

  • eps
    (depthchi, time)
    float64
    dask.array<chunksize=(7, 95277), meta=np.ndarray>

    long_name :

    turbulence dissipation rate

    standard_name :

    specific_turbulent_kinetic_energy_dissipation_in_sea_water

    ncei_name :

    turbulent kinetic energy (TKE) dissipation rate

    units :

    W kg-1

    FillValue :

    -9999

    valid_min :

    1e-12

    valid_max :

    9.999999999999999e-06

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    source :

    inferred from fast thermistor spectral scaling

    references :

    Moum J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 11.34 MiB 5.09 MiB Shape (7, 212302) (7, 95277) Count 78 Tasks 11 Chunks Type float64 numpy.ndarray

  • Jq
    (depthchi, time)
    float64
    dask.array<chunksize=(7, 95277), meta=np.ndarray>

    long_name :

    turbulent heat flux

    units :

    W m-2

    FillValue :

    -9999

    valid_min :

    -800.0

    valid_max :

    0.0

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    source :

    inferred from fast thermistor spectral scaling

    references :

    Moum J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    Array Chunk Bytes 11.34 MiB 5.09 MiB Shape (7, 212302) (7, 95277) Count 78 Tasks 11 Chunks Type float64 numpy.ndarray

  • shred2
    (time, depth)
    float64
    dask.array<chunksize=(33130, 61), meta=np.ndarray>

    long_name :

    $Sh_{red}^2$

    units :

    $s^{-2}$

    Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Count 35 Tasks 3 Chunks Type float64 numpy.ndarray

  • n2s2pdf
    (N2T_bins, S2_bins, enso_transition_phase)
    float64
    0.006507 0.003326 ... 0.0 0.0

    long_name :

    $P(S^2, 4N_T^2)$

    array([[[6.50718335e-03, 3.32593233e-03, 2.10241603e-02, ...,
             1.92471327e-03, 4.26669148e-03, 8.04793834e-03],
            [6.95424175e-03, 2.37566595e-03, 2.15369447e-02, ...,
             4.23436920e-03, 4.26669148e-03, 8.80243256e-03],
            [9.04051428e-03, 6.17673147e-03, 2.84595341e-02, ...,
             3.07954124e-03, 4.99812431e-03, 1.18204094e-02],
            ...,
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],
    
           [[7.84835855e-03, 5.70159828e-03, 1.97421993e-02, ...,
             2.30965593e-03, 7.19242278e-03, 8.29943642e-03],
            [9.88495792e-03, 4.75133190e-03, 2.64083965e-02, ...,
             3.84942655e-03, 8.77719391e-03, 1.06467518e-02],
            [1.18718841e-02, 3.32593233e-03, 2.99978873e-02, ...,
             5.00425451e-03, 1.13372088e-02, 1.22395729e-02],
    ...
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],
    
           [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            ...,
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]])

  • eps_n2s2
    (N2T_bins, S2_bins, enso_transition_phase)
    float64
    nan 2.034e-08 3.333e-08 ... nan nan

    long_name :

    turbulence dissipation rate

    standard_name :

    specific_turbulent_kinetic_energy_dissipation_in_sea_water

    ncei_name :

    turbulent kinetic energy (TKE) dissipation rate

    units :

    W kg-1

    FillValue :

    -9999

    valid_min :

    1e-12

    valid_max :

    9.999999999999999e-06

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    source :

    inferred from fast thermistor spectral scaling

    references :

    Moum J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    array([[[           nan, 2.03429245e-08, 3.33316402e-08, ...,
             4.04217379e-08, 2.03429245e-08,            nan],
            [           nan,            nan, 2.56887848e-08, ...,
                        nan, 8.29563644e-09,            nan],
            [           nan, 2.03429245e-08, 1.74830996e-08, ...,
                        nan, 1.75432060e-08,            nan],
            ...,
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan]],
    
           [[           nan, 2.03429245e-08, 2.62563255e-08, ...,
             4.04217379e-08, 2.83870225e-08,            nan],
            [           nan, 4.04217379e-08, 2.04634978e-08, ...,
             4.04217379e-08, 3.70951117e-08,            nan],
            [           nan,            nan, 2.11115998e-08, ...,
             4.04217379e-08, 2.95904940e-08,            nan],
    ...
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan]],
    
           [[           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            ...,
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan]]])

  • eps_ri
    (stat, Rig_T_bins, enso_2phase)
    float64
    6.847e-08 1.731e-07 ... 1.191e-07

    long_name :

    turbulence dissipation rate

    standard_name :

    specific_turbulent_kinetic_energy_dissipation_in_sea_water

    ncei_name :

    turbulent kinetic energy (TKE) dissipation rate

    units :

    W kg-1

    FillValue :

    -9999

    valid_min :

    1e-12

    valid_max :

    9.999999999999999e-06

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    crs

    source :

    inferred from fast thermistor spectral scaling

    references :

    Moum J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

    cell_methods :

    depth: point, time: mean

    platform :

    mooring

    instrument :

    chipod

    array([[[6.84689832e-08, 1.73056315e-07, 8.63225093e-08],
            [6.07764670e-08, 1.19899458e-07, 7.46213864e-08],
            [5.40214704e-08, 7.61291146e-08, 5.76651579e-08],
            [5.13152508e-08, 5.35653650e-08, 4.66238618e-08],
            [4.62280512e-08, 4.28818746e-08, 4.03153044e-08],
            [4.08235155e-08, 3.37893080e-08, 3.37038440e-08],
            [3.91735191e-08, 3.03913607e-08, 3.12840649e-08],
            [4.03018848e-08, 2.91192912e-08, 3.12221690e-08],
            [4.75944246e-08, 2.37189318e-08, 3.10042104e-08],
            [3.71924802e-08, 2.54268936e-08, 2.92288978e-08],
            [4.12688840e-08, 2.73037251e-08, 3.00062865e-08],
            [3.34608379e-08, 2.26778416e-08, 2.51965247e-08],
            [4.39860454e-08, 2.70428570e-08, 3.04069659e-08],
            [4.29868758e-08, 2.24152327e-08, 2.77569237e-08],
            [3.53232832e-08, 2.18226508e-08, 2.55120963e-08],
            [5.11616106e-08, 2.05947297e-08, 3.12250752e-08],
            [3.70582827e-08, 1.77766348e-08, 2.40288483e-08],
            [4.89518976e-08, 2.53817651e-08, 3.35931444e-08],
            [4.56671575e-08, 2.82929682e-08, 3.13209376e-08],
            [4.64876662e-08, 2.04875416e-08, 2.86485287e-08]],
    ...
           [[2.18304287e-07, 4.04225514e-07, 2.58054291e-07],
            [1.83965740e-07, 2.73378471e-07, 2.04971123e-07],
            [1.63182029e-07, 1.90751018e-07, 1.62840681e-07],
            [1.62984331e-07, 1.53367937e-07, 1.41413315e-07],
            [1.56989403e-07, 1.38801312e-07, 1.33669664e-07],
            [1.25707118e-07, 1.18304373e-07, 1.07317489e-07],
            [1.20101049e-07, 1.28587864e-07, 1.08632953e-07],
            [1.31913890e-07, 1.22667936e-07, 1.11143914e-07],
            [2.27458845e-07, 7.68759132e-08, 1.41048007e-07],
            [1.41834151e-07, 9.27363902e-08, 1.08190489e-07],
            [1.78506564e-07, 1.11239445e-07, 1.25558252e-07],
            [1.09851653e-07, 7.64569502e-08, 8.16981403e-08],
            [2.18725949e-07, 2.04519484e-07, 1.75659025e-07],
            [1.64568020e-07, 9.79191596e-08, 1.12410431e-07],
            [1.14755355e-07, 6.44097713e-08, 8.31343668e-08],
            [1.84721363e-07, 7.76673187e-08, 1.22864468e-07],
            [1.70899077e-07, 5.69893673e-08, 1.07775069e-07],
            [2.82073743e-07, 1.89226388e-07, 2.07505942e-07],
            [1.62819437e-07, 1.26439710e-07, 1.23292278e-07],
            [1.86040041e-07, 6.20383557e-08, 1.19081080e-07]]])

• Attributes: (0)

np.log10(mom6140.eps_ri).sel(stat="mean").plot(hue="enso_2phase")
np.log10(tao_gridded.eps_ri).sel(stat="mean").plot(hue="enso_2phase")

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Input In [88], in <cell line: 1>()
----> 1 np.log10(mom6140.eps_ri).sel(stat="mean").plot(hue="enso_2phase")
      2 np.log10(tao_gridded.eps_ri).sel(stat="mean").plot(hue="enso_2phase")

NameError: name 'mom6140' is not defined

np.log10(tao_gridded.eps_n2s2).plot(robust=True, col="enso_transition_phase")

<xarray.plot.facetgrid.FacetGrid at 0x2acdb3799ae0>

Seasonal mean heat flux

remapped.Jq.load()

<xarray.DataArray 'Jq' (time: 174000, zeuc: 100)>
array([[        nan,         nan, -0.07841657, ...,         nan,
                nan,         nan],
       [        nan,         nan, -0.07973828, ...,         nan,
                nan,         nan],
       [        nan,         nan, -0.08094554, ...,         nan,
                nan,         nan],
       ...,
       [        nan, -0.07447129,         nan, ...,         nan,
                nan,         nan],
       [        nan, -0.07471326,         nan, ...,         nan,
                nan,         nan],
       [        nan, -0.07509062,         nan, ...,         nan,
                nan,         nan]])
Coordinates:
    latitude   float64 0.025
    longitude  float64 -140.0
  * time       (time) datetime64[ns] 1998-12-31T18:00:00 ... 2018-11-06T17:00:00
  * zeuc       (zeuc) float64 -247.5 -242.5 -237.5 -232.5 ... 237.5 242.5 247.5

xarray.DataArray

'Jq'

• time: 174000
• zeuc: 100

• nan nan -0.07842 nan -0.06425 nan -0.05399 ... nan nan nan nan nan nan

  array([[        nan,         nan, -0.07841657, ...,         nan,
                  nan,         nan],
         [        nan,         nan, -0.07973828, ...,         nan,
                  nan,         nan],
         [        nan,         nan, -0.08094554, ...,         nan,
                  nan,         nan],
         ...,
         [        nan, -0.07447129,         nan, ...,         nan,
                  nan,         nan],
         [        nan, -0.07471326,         nan, ...,         nan,
                  nan,         nan],
         [        nan, -0.07509062,         nan, ...,         nan,
                  nan,         nan]])

• Coordinates: (4)
  • latitude
    ()
    float64
    0.025

    array(0.025)

  • longitude
    ()
    float64
    -140.0

    array(-140.0249939)

  • time
    (time)
    datetime64[ns]
    1998-12-31T18:00:00 ... 2018-11-...

    array(['1998-12-31T18:00:00.000000000', '1998-12-31T19:00:00.000000000',
           '1998-12-31T20:00:00.000000000', ..., '2018-11-06T15:00:32.000000000',
           '2018-11-06T16:00:16.000000000', '2018-11-06T17:00:00.000000000'],
          dtype='datetime64[ns]')

  • zeuc
    (zeuc)
    float64
    -247.5 -242.5 ... 242.5 247.5

    axis :

    Z

    units :

    m

    array([-247.5, -242.5, -237.5, -232.5, -227.5, -222.5, -217.5, -212.5, -207.5,
           -202.5, -197.5, -192.5, -187.5, -182.5, -177.5, -172.5, -167.5, -162.5,
           -157.5, -152.5, -147.5, -142.5, -137.5, -132.5, -127.5, -122.5, -117.5,
           -112.5, -107.5, -102.5,  -97.5,  -92.5,  -87.5,  -82.5,  -77.5,  -72.5,
            -67.5,  -62.5,  -57.5,  -52.5,  -47.5,  -42.5,  -37.5,  -32.5,  -27.5,
            -22.5,  -17.5,  -12.5,   -7.5,   -2.5,    2.5,    7.5,   12.5,   17.5,
             22.5,   27.5,   32.5,   37.5,   42.5,   47.5,   52.5,   57.5,   62.5,
             67.5,   72.5,   77.5,   82.5,   87.5,   92.5,   97.5,  102.5,  107.5,
            112.5,  117.5,  122.5,  127.5,  132.5,  137.5,  142.5,  147.5,  152.5,
            157.5,  162.5,  167.5,  172.5,  177.5,  182.5,  187.5,  192.5,  197.5,
            202.5,  207.5,  212.5,  217.5,  222.5,  227.5,  232.5,  237.5,  242.5,
            247.5])

• Attributes: (0)

import hvplot.xarray

(
    remapped.Jq.groupby("time.season").mean()
    .reindex(season=["DJF", "MAM", "JJA", "SON"])
    .cf.plot.line(col="season")
)

<xarray.plot.facetgrid.FacetGrid at 0x2affc38316c0>

Test out available variables

1. KPP_Kheat is non-zero only in KPP_OBL

(mom6140.Tflx_dia_diff * 1025 * 4000).cf.plot(robust=True)

<matplotlib.collections.QuadMesh at 0x2ba58a988fa0>

mom6140.Kv_u.cf.plot(norm=mpl.colors.LogNorm(1e-6, 1e-2))

<matplotlib.collections.QuadMesh at 0x2ba58a007cd0>

mom6140.h.median("time").plot()
mom6140.h.mean("time").plot()

[<matplotlib.lines.Line2D at 0x2ba58a547c10>]

mom6140.Kd_heat.cf.plot(norm=mpl.colors.LogNorm(1e-6, 1e-2))
mom6140.KPP_OBLdepth.plot(color='r')

plt.figure()
mom6140.KPP_kheat.cf.plot(norm=mpl.colors.LogNorm(1e-6, 1e-2))
mom6140.KPP_OBLdepth.plot(color='r')

plt.figure()
(mom6140.KPP_kheat - mom6140.Kd_heat).cf.plot(robust=True, cmap=mpl.cm.mpl.cm.Reds_r, vmax=0)

<matplotlib.collections.QuadMesh at 0x2ba58a8c38b0>

Experiment with manual combining

import intake
from intake.source.utils import reverse_format

years = []
tiles = []
for file in files[:10]:
    p = pathlib.Path(file)
    params = reverse_format("__{year}.nc.{tile}", p.stem + p.suffix)
    years.append(params["year"])
    tiles.append(params["tile"])
years, tiles

import toolz as tlz

bytile = {}
for tile, v in tlz.groupby(tileid, files).items():
    bytile[tile] = xr.concat(read_raw_files(v, parallel=True), dim="time")



print("\n".join([hash(ds.yh.data.tolist()) for ds in list(bytile.values())]))

from functools import partial


def hash_coords(ds, axis):
    dims = ds.cf.axes[axis]
    data = np.concatenate(
        [ds[dim].data
        for dim in dims]
    )
    return hash(tuple(data))


grouped = bytile

for axis, concat_axis in [("X", "Y"), ("Y", "X")]:
    grouped = tlz.groupby(partial(hash_coords, axis=axis), grouped.values())
    grouped = {
        k: cfconcat(v, axis=concat_axis, join='exact') for k, v in grouped.items()
    }
    break 
grouped

cfconcat(list(grouped.values()), "X")

combined = xr.combine_by_coords(list(bytile.values()), combine_attrs="override")

def raise_if_bad_index(combined):
    bad = []
    for idx in combined.indexes:
        index = combined.indexes[idx]
        if not index.is_monotonic or index.has_duplicates:
            bad.append(idx)
    if bad:
        raise ValueError(f"Indexes {idx} are either not monotonic or have duplicates")

def tileid(path):
    p = pathlib.Path(path)
    #print(p.suffix)
    #params = reverse_format("__{year}.nc.{tile}", p.stem + p.suffix)
    return int(p.suffix[1:]) #params["tile"]
    #years.append(params["year"])
    #tiles.append(params["tile"])
    
sorted_files = sorted(files, key=tileid)
for tile, files in groupby(sorted_files, tileid):
    print(tile, len(list(files)))

Test out ONI

Phase labeling

oni = pump.obs.process_oni().sel(time=slice("2005-Sep", None))
enso_transition = mixpods.make_enso_transition_mask(oni)
mixpods.plot_enso_transition(oni, enso_transition)

Replicate ONI calculation

close enough!

ersst = xr.tutorial.open_dataset("ersstv5")

monthlyersst = (
    ersst.sst.cf.sortby("Y")
    .cf.sel(latitude=slice(-5, 5), longitude=slice(360-170, 360-120))
    .cf.mean(["X", "Y"])
    .resample(time="M").mean()
    .load()
)

expected = mixpods.calc_oni(monthlyersst)

actual = pump.obs.process_oni()
actual.plot()
expected.plot()

(actual-expected).plot()

[<matplotlib.lines.Line2D at 0x2b6d2ec04580>]

Comparing to Warner and Moum code

chipod.eps.sel(time="2007-01-14 10:35:00", method="nearest").load()

<xarray.DataArray 'eps' (depth: 5)>
nan 3.929e-07 2.846e-07 2.234e-07 4.491e-07
Coordinates:
    time        datetime64[ns] 2007-01-14T11:00:00
  * depth       (depth) float64 -69.0 -59.0 -49.0 -39.0 -29.0
    timeSeries  (depth) float64 69.0 59.0 49.0 39.0 29.0
    lat         (depth) float64 0.0 0.0 0.0 0.0 0.0
    lon         (depth) float64 -140.0 -140.0 -140.0 -140.0 -140.0
Attributes:
    long_name:              turbulence dissipation rate
    standard_name:          specific_turbulent_kinetic_energy_dissipation_in_...
    ncei_name:              turbulent kinetic energy (TKE) dissipation rate
    units:                  W kg-1
    FillValue:              -9999
    valid_min:              1e-12
    valid_max:              9.999999999999999e-06
    coverage_content_type:  physicalMeasurement
    grid_mapping:           crs
    source:                 inferred from fast thermistor spectral scaling
    references:             Moum J.N. and J.D. Nash, Mixing measurements on a...
    cell_methods:           depth: point, time: mean
    platform:               mooring
    instrument:             chipod

xarray.DataArray

'eps'

• depth: 5

• nan 3.929e-07 2.846e-07 2.234e-07 4.491e-07

  array([           nan, 3.92899514e-07, 2.84615955e-07, 2.23436769e-07,
         4.49053774e-07])

• Coordinates: (5)
  • time
    ()
    datetime64[ns]
    2007-01-14T11:00:00

    long_name :

    Time, UTC

    standard_name :

    time

    axis :

    T

    array('2007-01-14T11:00:00.000000000', dtype='datetime64[ns]')

  • depth
    (depth)
    float64
    -69.0 -59.0 -49.0 -39.0 -29.0

    long_name :

    depths of sensors along the mooring line

    standard_name :

    depth

    units :

    m

    axis :

    Z

    positive :

    up

    valid_min :

    29.0

    valid_max :

    119.0

    array([-69., -59., -49., -39., -29.])

  • timeSeries
    (depth)
    float64
    69.0 59.0 49.0 39.0 29.0

    long_name :

    depths of Time series of T, dTdz, Nsqr, chi, epsilon, and Jq along a mooring line at 0-140W

    cf_role :

    timeseries_id

    array([69., 59., 49., 39., 29.])

  • lat
    (depth)
    float64
    0.0 0.0 0.0 0.0 0.0

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    valid_min :

    0.0

    valid_max :

    0.0

    array([0., 0., 0., 0., 0.])

  • lon
    (depth)
    float64
    -140.0 -140.0 -140.0 -140.0 -140.0

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    axis :

    X

    valid_min :

    -140.0

    valid_max :

    -140.0

    array([-140., -140., -140., -140., -140.])

• Attributes: (14)

  long_name :

  turbulence dissipation rate

  standard_name :

  specific_turbulent_kinetic_energy_dissipation_in_sea_water

  ncei_name :

  turbulent kinetic energy (TKE) dissipation rate

  units :

  W kg-1

  FillValue :

  -9999

  valid_min :

  1e-12

  valid_max :

  9.999999999999999e-06

  coverage_content_type :

  physicalMeasurement

  grid_mapping :

  crs

  source :

  inferred from fast thermistor spectral scaling

  references :

  Moum J.N. and J.D. Nash, Mixing measurements on an equatorial ocean mooring, J. Atmos. and Oceanic Tech., 26, 317-336, 2009. pdf: http://mixing.coas.oregonstate.edu/papers/mixing_measurements.pdf

  cell_methods :

  depth: point, time: mean

  platform :

  mooring

  instrument :

  chipod

tao_gridded.enso_transition.loc[{"time": slice("2015-12", "2016-01")}]

<xarray.DataArray 'enso_transition' (time: 1488)>
'El-Nino warm' 'El-Nino warm' 'El-Nino warm' ... 'El-Nino warm' 'El-Nino warm'
Coordinates: (12/13)
    deepest             (time) float64 -300.0 -300.0 -300.0 ... -300.0 -300.0
    eucmax              (time) float64 -135.0 -140.0 -135.0 ... -140.0 -135.0
    latitude            float32 0.0
    longitude           float32 -140.0
    mld                 (time) float64 -9.0 -8.0 -8.0 -8.0 ... nan nan nan nan
    mldT                (time) float64 -10.0 -8.0 -8.0 -8.0 ... nan nan nan nan
    ...                  ...
    shallowest          (time) float64 -5.0 -5.0 -5.0 -5.0 ... -10.0 -10.0 -10.0
  * time                (time) datetime64[ns] 2015-12-01 ... 2016-01-31T23:00:00
    oni                 (time) float32 2.53 2.53 2.53 2.53 ... 2.53 2.53 2.53
    warm_mask           (time) bool True True True True ... False False False
    cool_mask           (time) bool False False False False ... True True True
    enso_transition     (time) <U12 'El-Nino warm' ... 'El-Nino warm'
Attributes:
    description:  Warner & Moum (2019) ENSO transition phase; El-Nino = ONI >...

xarray.DataArray

'enso_transition'

• time: 1488

• 'El-Nino warm' 'El-Nino warm' ... 'El-Nino warm' 'El-Nino warm'

  array(['El-Nino warm', 'El-Nino warm', 'El-Nino warm', ...,
         'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12')

• Coordinates: (13)
  • deepest
    (time)
    float64
    -300.0 -300.0 ... -300.0 -300.0

    description :

    Deepest depth with a valid observation

    units :

    m

    array([-300., -300., -300., ..., -300., -300., -300.])

  • eucmax
    (time)
    float64
    -135.0 -140.0 ... -140.0 -135.0

    units :

    m

    long_name :

    EUC maximum

    positive :

    up

    array([-135., -140., -135., ..., -140., -140., -135.])

  • latitude
    ()
    float32
    0.0

    array(0., dtype=float32)

  • longitude
    ()
    float32
    -140.0

    array(-140., dtype=float32)

  • mld
    (time)
    float64
    -9.0 -8.0 -8.0 -8.0 ... nan nan nan

    long_name :

    $z_{MLD}$

    units :

    m

    array([-9., -8., -8., ..., nan, nan, nan])

  • mldT
    (time)
    float64
    -10.0 -8.0 -8.0 ... nan nan nan

    long_name :

    MLD$_θ$

    units :

    m

    description :

    Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

    array([-10.,  -8.,  -8., ...,  nan,  nan,  nan])

  • reference_pressure
    ()
    int64
    0

    array(0)

  • shallowest
    (time)
    float64
    -5.0 -5.0 -5.0 ... -10.0 -10.0

    array([ -5.,  -5.,  -5., ..., -10., -10., -10.])

  • time
    (time)
    datetime64[ns]
    2015-12-01 ... 2016-01-31T23:00:00

    array(['2015-12-01T00:00:00.000000000', '2015-12-01T01:00:00.000000000',
           '2015-12-01T02:00:00.000000000', ..., '2016-01-31T21:00:00.000000000',
           '2016-01-31T22:00:00.000000000', '2016-01-31T23:00:00.000000000'],
          dtype='datetime64[ns]')

  • oni
    (time)
    float32
    2.53 2.53 2.53 ... 2.53 2.53 2.53

    array([2.53, 2.53, 2.53, ..., 2.53, 2.53, 2.53], dtype=float32)

  • warm_mask
    (time)
    bool
    True True True ... False False

    array([ True,  True,  True, ..., False, False, False])

  • cool_mask
    (time)
    bool
    False False False ... True True

    array([False, False, False, ...,  True,  True,  True])

  • enso_transition
    (time)
    <U12
    'El-Nino warm' ... 'El-Nino warm'

    description :

    Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

    array(['El-Nino warm', 'El-Nino warm', 'El-Nino warm', ...,
           'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12')

• Attributes: (1)

  description :

  Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months